# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import warnings
from math import ceil
from typing import Iterable, List, Optional, Tuple, Union

import numpy as np

from .image_utils import (
    ChannelDimension,
    ImageInput,
    get_channel_dimension_axis,
    get_image_size,
    infer_channel_dimension_format,
)
from .utils import ExplicitEnum, TensorType, is_jax_tensor, is_tf_tensor, is_torch_tensor
from .utils.import_utils import (
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_torchvision_available,
    is_vision_available,
    requires_backends,
)


if is_vision_available():
    import PIL

    from .image_utils import PILImageResampling

if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf

if is_flax_available():
    import jax.numpy as jnp

if is_torchvision_available():
    from torchvision.transforms import functional as F


def to_channel_dimension_format(
    image: np.ndarray,
    channel_dim: Union[ChannelDimension, str],
    input_channel_dim: Optional[Union[ChannelDimension, str]] = None,
) -> np.ndarray:
    """
    Converts `image` to the channel dimension format specified by `channel_dim`.

    Args:
        image (`numpy.ndarray`):
            The image to have its channel dimension set.
        channel_dim (`ChannelDimension`):
            The channel dimension format to use.
        input_channel_dim (`ChannelDimension`, *optional*):
            The channel dimension format of the input image. If not provided, it will be inferred from the input image.

    Returns:
        `np.ndarray`: The image with the channel dimension set to `channel_dim`.
    """
    if not isinstance(image, np.ndarray):
        raise TypeError(f"Input image must be of type np.ndarray, got {type(image)}")

    if input_channel_dim is None:
        input_channel_dim = infer_channel_dimension_format(image)

    target_channel_dim = ChannelDimension(channel_dim)
    if input_channel_dim == target_channel_dim:
        return image

    if target_channel_dim == ChannelDimension.FIRST:
        image = image.transpose((2, 0, 1))
    elif target_channel_dim == ChannelDimension.LAST:
        image = image.transpose((1, 2, 0))
    else:
        raise ValueError("Unsupported channel dimension format: {}".format(channel_dim))

    return image


def rescale(
    image: np.ndarray,
    scale: float,
    data_format: Optional[ChannelDimension] = None,
    dtype: np.dtype = np.float32,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
    """
    Rescales `image` by `scale`.

    Args:
        image (`np.ndarray`):
            The image to rescale.
        scale (`float`):
            The scale to use for rescaling the image.
        data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the image. If not provided, it will be the same as the input image.
        dtype (`np.dtype`, *optional*, defaults to `np.float32`):
            The dtype of the output image. Defaults to `np.float32`. Used for backwards compatibility with feature
            extractors.
        input_data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the input image. If not provided, it will be inferred from the input image.

    Returns:
        `np.ndarray`: The rescaled image.
    """
    if not isinstance(image, np.ndarray):
        raise TypeError(f"Input image must be of type np.ndarray, got {type(image)}")

    rescaled_image = image * scale
    if data_format is not None:
        rescaled_image = to_channel_dimension_format(rescaled_image, data_format, input_data_format)

    rescaled_image = rescaled_image.astype(dtype)

    return rescaled_image


def _rescale_for_pil_conversion(image):
    """
    Detects whether or not the image needs to be rescaled before being converted to a PIL image.

    The assumption is that if the image is of type `np.float` and all values are between 0 and 1, it needs to be
    rescaled.
    """
    if image.dtype == np.uint8:
        do_rescale = False
    elif np.allclose(image, image.astype(int)):
        if np.all(0 <= image) and np.all(image <= 255):
            do_rescale = False
        else:
            raise ValueError(
                "The image to be converted to a PIL image contains values outside the range [0, 255], "
                f"got [{image.min()}, {image.max()}] which cannot be converted to uint8."
            )
    elif np.all(0 <= image) and np.all(image <= 1):
        do_rescale = True
    else:
        raise ValueError(
            "The image to be converted to a PIL image contains values outside the range [0, 1], "
            f"got [{image.min()}, {image.max()}] which cannot be converted to uint8."
        )
    return do_rescale


def to_pil_image(
    image: Union[np.ndarray, "PIL.Image.Image", "torch.Tensor", "tf.Tensor", "jnp.ndarray"],
    do_rescale: Optional[bool] = None,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> "PIL.Image.Image":
    """
    Converts `image` to a PIL Image. Optionally rescales it and puts the channel dimension back as the last axis if
    needed.

    Args:
        image (`PIL.Image.Image` or `numpy.ndarray` or `torch.Tensor` or `tf.Tensor`):
            The image to convert to the `PIL.Image` format.
        do_rescale (`bool`, *optional*):
            Whether or not to apply the scaling factor (to make pixel values integers between 0 and 255). Will default
            to `True` if the image type is a floating type and casting to `int` would result in a loss of precision,
            and `False` otherwise.
        input_data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the input image. If unset, will use the inferred format from the input.

    Returns:
        `PIL.Image.Image`: The converted image.
    """
    requires_backends(to_pil_image, ["vision"])

    if isinstance(image, PIL.Image.Image):
        return image

    # Convert all tensors to numpy arrays before converting to PIL image
    if is_torch_tensor(image) or is_tf_tensor(image):
        image = image.numpy()
    elif is_jax_tensor(image):
        image = np.array(image)
    elif not isinstance(image, np.ndarray):
        raise ValueError("Input image type not supported: {}".format(type(image)))

    # If the channel has been moved to first dim, we put it back at the end.
    image = to_channel_dimension_format(image, ChannelDimension.LAST, input_data_format)

    # If there is a single channel, we squeeze it, as otherwise PIL can't handle it.
    image = np.squeeze(image, axis=-1) if image.shape[-1] == 1 else image

    # PIL.Image can only store uint8 values so we rescale the image to be between 0 and 255 if needed.
    do_rescale = _rescale_for_pil_conversion(image) if do_rescale is None else do_rescale

    if do_rescale:
        image = rescale(image, 255)

    image = image.astype(np.uint8)
    return PIL.Image.fromarray(image)


# Logic adapted from torchvision resizing logic: https://github.com/pytorch/vision/blob/511924c1ced4ce0461197e5caa64ce5b9e558aab/torchvision/transforms/functional.py#L366
def get_resize_output_image_size(
    input_image: np.ndarray,
    size: Union[int, Tuple[int, int], List[int], Tuple[int]],
    default_to_square: bool = True,
    max_size: Optional[int] = None,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> tuple:
    """
    Find the target (height, width) dimension of the output image after resizing given the input image and the desired
    size.

    Args:
        input_image (`np.ndarray`):
            The image to resize.
        size (`int` or `Tuple[int, int]` or List[int] or `Tuple[int]`):
            The size to use for resizing the image. If `size` is a sequence like (h, w), output size will be matched to
            this.

            If `size` is an int and `default_to_square` is `True`, then image will be resized to (size, size). If
            `size` is an int and `default_to_square` is `False`, then 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).
        default_to_square (`bool`, *optional*, defaults to `True`):
            How to convert `size` when it is a single int. If set to `True`, the `size` will be converted to a square
            (`size`,`size`). If set to `False`, will replicate
            [`torchvision.transforms.Resize`](https://pytorch.org/vision/stable/transforms.html#torchvision.transforms.Resize)
            with support for resizing only the smallest edge and providing an optional `max_size`.
        max_size (`int`, *optional*):
            The maximum allowed for the longer edge of the resized image: if the longer edge of the image is greater
            than `max_size` after being resized according to `size`, then the image is resized again so that the longer
            edge is equal to `max_size`. As a result, `size` might be overruled, i.e the smaller edge may be shorter
            than `size`. Only used if `default_to_square` is `False`.
        input_data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the input image. If unset, will use the inferred format from the input.

    Returns:
        `tuple`: The target (height, width) dimension of the output image after resizing.
    """
    if isinstance(size, (tuple, list)):
        if len(size) == 2:
            return tuple(size)
        elif len(size) == 1:
            # Perform same logic as if size was an int
            size = size[0]
        else:
            raise ValueError("size must have 1 or 2 elements if it is a list or tuple")

    if default_to_square:
        return (size, size)

    height, width = get_image_size(input_image, input_data_format)
    short, long = (width, height) if width <= height else (height, width)
    requested_new_short = size

    new_short, new_long = requested_new_short, int(requested_new_short * long / short)

    if max_size is not None:
        if max_size <= requested_new_short:
            raise ValueError(
                f"max_size = {max_size} must be strictly greater than the requested "
                f"size for the smaller edge size = {size}"
            )
        if new_long > max_size:
            new_short, new_long = int(max_size * new_short / new_long), max_size

    return (new_long, new_short) if width <= height else (new_short, new_long)


def resize(
    image: np.ndarray,
    size: Tuple[int, int],
    resample: "PILImageResampling" = None,
    reducing_gap: Optional[int] = None,
    data_format: Optional[ChannelDimension] = None,
    return_numpy: bool = True,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
    """
    Resizes `image` to `(height, width)` specified by `size` using the PIL library.

    Args:
        image (`np.ndarray`):
            The image to resize.
        size (`Tuple[int, int]`):
            The size to use for resizing the image.
        resample (`int`, *optional*, defaults to `PILImageResampling.BILINEAR`):
            The filter to user for resampling.
        reducing_gap (`int`, *optional*):
            Apply optimization by resizing the image in two steps. The bigger `reducing_gap`, the closer the result to
            the fair resampling. See corresponding Pillow documentation for more details.
        data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the output image. If unset, will use the inferred format from the input.
        return_numpy (`bool`, *optional*, defaults to `True`):
            Whether or not to return the resized image as a numpy array. If False a `PIL.Image.Image` object is
            returned.
        input_data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the input image. If unset, will use the inferred format from the input.

    Returns:
        `np.ndarray`: The resized image.
    """
    requires_backends(resize, ["vision"])

    resample = resample if resample is not None else PILImageResampling.BILINEAR

    if not len(size) == 2:
        raise ValueError("size must have 2 elements")

    # For all transformations, we want to keep the same data format as the input image unless otherwise specified.
    # The resized image from PIL will always have channels last, so find the input format first.
    if input_data_format is None:
        input_data_format = infer_channel_dimension_format(image)
    data_format = input_data_format if data_format is None else data_format

    # To maintain backwards compatibility with the resizing done in previous image feature extractors, we use
    # the pillow library to resize the image and then convert back to numpy
    do_rescale = False
    if not isinstance(image, PIL.Image.Image):
        do_rescale = _rescale_for_pil_conversion(image)
        image = to_pil_image(image, do_rescale=do_rescale, input_data_format=input_data_format)
    height, width = size
    # PIL images are in the format (width, height)
    resized_image = image.resize((width, height), resample=resample, reducing_gap=reducing_gap)

    if return_numpy:
        resized_image = np.array(resized_image)
        # If the input image channel dimension was of size 1, then it is dropped when converting to a PIL image
        # so we need to add it back if necessary.
        resized_image = np.expand_dims(resized_image, axis=-1) if resized_image.ndim == 2 else resized_image
        # The image is always in channels last format after converting from a PIL image
        resized_image = to_channel_dimension_format(
            resized_image, data_format, input_channel_dim=ChannelDimension.LAST
        )
        # If an image was rescaled to be in the range [0, 255] before converting to a PIL image, then we need to
        # rescale it back to the original range.
        resized_image = rescale(resized_image, 1 / 255) if do_rescale else resized_image
    return resized_image


def normalize(
    image: np.ndarray,
    mean: Union[float, Iterable[float]],
    std: Union[float, Iterable[float]],
    data_format: Optional[ChannelDimension] = None,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
    """
    Normalizes `image` using the mean and standard deviation specified by `mean` and `std`.

    image = (image - mean) / std

    Args:
        image (`np.ndarray`):
            The image to normalize.
        mean (`float` or `Iterable[float]`):
            The mean to use for normalization.
        std (`float` or `Iterable[float]`):
            The standard deviation to use for normalization.
        data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the output image. If unset, will use the inferred format from the input.
        input_data_format (`ChannelDimension`, *optional*):
            The channel dimension format of the input image. If unset, will use the inferred format from the input.
    """
    if not isinstance(image, np.ndarray):
        raise ValueError("image must be a numpy array")

    if input_data_format is None:
        input_data_format = infer_channel_dimension_format(image)

    channel_axis = get_channel_dimension_axis(image, input_data_format=input_data_format)
    num_channels = image.shape[channel_axis]

    # We cast to float32 to avoid errors that can occur when subtracting uint8 values.
    # We preserve the original dtype if it is a float type to prevent upcasting float16.
    if not np.issubdtype(image.dtype, np.floating):
        image = image.astype(np.float32)

    if isinstance(mean, Iterable):
        if len(mean) != num_channels:
            raise ValueError(f"mean must have {num_channels} elements if it is an iterable, got {len(mean)}")
    else:
        mean = [mean] * num_channels
    mean = np.array(mean, dtype=image.dtype)

    if isinstance(std, Iterable):
        if len(std) != num_channels:
            raise ValueError(f"std must have {num_channels} elements if it is an iterable, got {len(std)}")
    else:
        std = [std] * num_channels
    std = np.array(std, dtype=image.dtype)

    if input_data_format == ChannelDimension.LAST:
        image = (image - mean) / std
    else:
        image = ((image.T - mean) / std).T

    image = to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image
    return image


def center_crop(
    image: np.ndarray,
    size: Tuple[int, int],
    data_format: Optional[Union[str, ChannelDimension]] = None,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
    return_numpy: Optional[bool] = None,
) -> np.ndarray:
    """
    Crops the `image` to the specified `size` using a center crop. Note that if the image is too small to be cropped to
    the size given, it will be padded (so the returned result will always be of size `size`).

    Args:
        image (`np.ndarray`):
            The image to crop.
        size (`Tuple[int, int]`):
            The target size for the cropped image.
        data_format (`str` or `ChannelDimension`, *optional*):
            The channel dimension format for the output image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            If unset, will use the inferred format of the input image.
        input_data_format (`str` or `ChannelDimension`, *optional*):
            The channel dimension format for the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            If unset, will use the inferred format of the input image.
        return_numpy (`bool`, *optional*):
            Whether or not to return the cropped image as a numpy array. Used for backwards compatibility with the
            previous ImageFeatureExtractionMixin method.
                - Unset: will return the same type as the input image.
                - `True`: will return a numpy array.
                - `False`: will return a `PIL.Image.Image` object.
    Returns:
        `np.ndarray`: The cropped image.
    """
    requires_backends(center_crop, ["vision"])

    if return_numpy is not None:
        warnings.warn("return_numpy is deprecated and will be removed in v.4.33", FutureWarning)

    return_numpy = True if return_numpy is None else return_numpy

    if not isinstance(image, np.ndarray):
        raise TypeError(f"Input image must be of type np.ndarray, got {type(image)}")

    if not isinstance(size, Iterable) or len(size) != 2:
        raise ValueError("size must have 2 elements representing the height and width of the output image")

    if input_data_format is None:
        input_data_format = infer_channel_dimension_format(image)
    output_data_format = data_format if data_format is not None else input_data_format

    # We perform the crop in (C, H, W) format and then convert to the output format
    image = to_channel_dimension_format(image, ChannelDimension.FIRST, input_data_format)

    orig_height, orig_width = get_image_size(image, ChannelDimension.FIRST)
    crop_height, crop_width = size
    crop_height, crop_width = int(crop_height), int(crop_width)

    # In case size is odd, (image_shape[0] + size[0]) // 2 won't give the proper result.
    top = (orig_height - crop_height) // 2
    bottom = top + crop_height
    # In case size is odd, (image_shape[1] + size[1]) // 2 won't give the proper result.
    left = (orig_width - crop_width) // 2
    right = left + crop_width

    # Check if cropped area is within image boundaries
    if top >= 0 and bottom <= orig_height and left >= 0 and right <= orig_width:
        image = image[..., top:bottom, left:right]
        image = to_channel_dimension_format(image, output_data_format, ChannelDimension.FIRST)
        return image

    # Otherwise, we may need to pad if the image is too small. Oh joy...
    new_height = max(crop_height, orig_height)
    new_width = max(crop_width, orig_width)
    new_shape = image.shape[:-2] + (new_height, new_width)
    new_image = np.zeros_like(image, shape=new_shape)

    # If the image is too small, pad it with zeros
    top_pad = ceil((new_height - orig_height) / 2)
    bottom_pad = top_pad + orig_height
    left_pad = ceil((new_width - orig_width) / 2)
    right_pad = left_pad + orig_width
    new_image[..., top_pad:bottom_pad, left_pad:right_pad] = image

    top += top_pad
    bottom += top_pad
    left += left_pad
    right += left_pad

    new_image = new_image[..., max(0, top) : min(new_height, bottom), max(0, left) : min(new_width, right)]
    new_image = to_channel_dimension_format(new_image, output_data_format, ChannelDimension.FIRST)

    if not return_numpy:
        new_image = to_pil_image(new_image)

    return new_image


def _center_to_corners_format_torch(bboxes_center: "torch.Tensor") -> "torch.Tensor":
    center_x, center_y, width, height = bboxes_center.unbind(-1)
    bbox_corners = torch.stack(
        # top left x, top left y, bottom right x, bottom right y
        [(center_x - 0.5 * width), (center_y - 0.5 * height), (center_x + 0.5 * width), (center_y + 0.5 * height)],
        dim=-1,
    )
    return bbox_corners


def _center_to_corners_format_numpy(bboxes_center: np.ndarray) -> np.ndarray:
    center_x, center_y, width, height = bboxes_center.T
    bboxes_corners = np.stack(
        # top left x, top left y, bottom right x, bottom right y
        [center_x - 0.5 * width, center_y - 0.5 * height, center_x + 0.5 * width, center_y + 0.5 * height],
        axis=-1,
    )
    return bboxes_corners


def _center_to_corners_format_tf(bboxes_center: "tf.Tensor") -> "tf.Tensor":
    center_x, center_y, width, height = tf.unstack(bboxes_center, axis=-1)
    bboxes_corners = tf.stack(
        # top left x, top left y, bottom right x, bottom right y
        [center_x - 0.5 * width, center_y - 0.5 * height, center_x + 0.5 * width, center_y + 0.5 * height],
        axis=-1,
    )
    return bboxes_corners


# 2 functions below inspired by https://github.com/facebookresearch/detr/blob/master/util/box_ops.py
def center_to_corners_format(bboxes_center: TensorType) -> TensorType:
    """
    Converts bounding boxes from center format to corners format.

    center format: contains the coordinate for the center of the box and its width, height dimensions
        (center_x, center_y, width, height)
    corners format: contains the coodinates for the top-left and bottom-right corners of the box
        (top_left_x, top_left_y, bottom_right_x, bottom_right_y)
    """
    # Function is used during model forward pass, so we use the input framework if possible, without
    # converting to numpy
    if is_torch_tensor(bboxes_center):
        return _center_to_corners_format_torch(bboxes_center)
    elif isinstance(bboxes_center, np.ndarray):
        return _center_to_corners_format_numpy(bboxes_center)
    elif is_tf_tensor(bboxes_center):
        return _center_to_corners_format_tf(bboxes_center)

    raise ValueError(f"Unsupported input type {type(bboxes_center)}")


def _corners_to_center_format_torch(bboxes_corners: "torch.Tensor") -> "torch.Tensor":
    top_left_x, top_left_y, bottom_right_x, bottom_right_y = bboxes_corners.unbind(-1)
    b = [
        (top_left_x + bottom_right_x) / 2,  # center x
        (top_left_y + bottom_right_y) / 2,  # center y
        (bottom_right_x - top_left_x),  # width
        (bottom_right_y - top_left_y),  # height
    ]
    return torch.stack(b, dim=-1)


def _corners_to_center_format_numpy(bboxes_corners: np.ndarray) -> np.ndarray:
    top_left_x, top_left_y, bottom_right_x, bottom_right_y = bboxes_corners.T
    bboxes_center = np.stack(
        [
            (top_left_x + bottom_right_x) / 2,  # center x
            (top_left_y + bottom_right_y) / 2,  # center y
            (bottom_right_x - top_left_x),  # width
            (bottom_right_y - top_left_y),  # height
        ],
        axis=-1,
    )
    return bboxes_center


def _corners_to_center_format_tf(bboxes_corners: "tf.Tensor") -> "tf.Tensor":
    top_left_x, top_left_y, bottom_right_x, bottom_right_y = tf.unstack(bboxes_corners, axis=-1)
    bboxes_center = tf.stack(
        [
            (top_left_x + bottom_right_x) / 2,  # center x
            (top_left_y + bottom_right_y) / 2,  # center y
            (bottom_right_x - top_left_x),  # width
            (bottom_right_y - top_left_y),  # height
        ],
        axis=-1,
    )
    return bboxes_center


def corners_to_center_format(bboxes_corners: TensorType) -> TensorType:
    """
    Converts bounding boxes from corners format to center format.

    corners format: contains the coordinates for the top-left and bottom-right corners of the box
        (top_left_x, top_left_y, bottom_right_x, bottom_right_y)
    center format: contains the coordinate for the center of the box and its the width, height dimensions
        (center_x, center_y, width, height)
    """
    # Inverse function accepts different input types so implemented here too
    if is_torch_tensor(bboxes_corners):
        return _corners_to_center_format_torch(bboxes_corners)
    elif isinstance(bboxes_corners, np.ndarray):
        return _corners_to_center_format_numpy(bboxes_corners)
    elif is_tf_tensor(bboxes_corners):
        return _corners_to_center_format_tf(bboxes_corners)

    raise ValueError(f"Unsupported input type {type(bboxes_corners)}")


# 2 functions below copied from https://github.com/cocodataset/panopticapi/blob/master/panopticapi/utils.py
# Copyright (c) 2018, Alexander Kirillov
# All rights reserved.
def rgb_to_id(color):
    """
    Converts RGB color to unique ID.
    """
    if isinstance(color, np.ndarray) and len(color.shape) == 3:
        if color.dtype == np.uint8:
            color = color.astype(np.int32)
        return color[:, :, 0] + 256 * color[:, :, 1] + 256 * 256 * color[:, :, 2]
    return int(color[0] + 256 * color[1] + 256 * 256 * color[2])


def id_to_rgb(id_map):
    """
    Converts unique ID to RGB color.
    """
    if isinstance(id_map, np.ndarray):
        id_map_copy = id_map.copy()
        rgb_shape = tuple(list(id_map.shape) + [3])
        rgb_map = np.zeros(rgb_shape, dtype=np.uint8)
        for i in range(3):
            rgb_map[..., i] = id_map_copy % 256
            id_map_copy //= 256
        return rgb_map
    color = []
    for _ in range(3):
        color.append(id_map % 256)
        id_map //= 256
    return color


class PaddingMode(ExplicitEnum):
    """
    Enum class for the different padding modes to use when padding images.
    """

    CONSTANT = "constant"
    REFLECT = "reflect"
    REPLICATE = "replicate"
    SYMMETRIC = "symmetric"


def pad(
    image: np.ndarray,
    padding: Union[int, Tuple[int, int], Iterable[Tuple[int, int]]],
    mode: PaddingMode = PaddingMode.CONSTANT,
    constant_values: Union[float, Iterable[float]] = 0.0,
    data_format: Optional[Union[str, ChannelDimension]] = None,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
    """
    Pads the `image` with the specified (height, width) `padding` and `mode`.

    Args:
        image (`np.ndarray`):
            The image to pad.
        padding (`int` or `Tuple[int, int]` or `Iterable[Tuple[int, int]]`):
            Padding to apply to the edges of the height, width axes. Can be one of three formats:
            - `((before_height, after_height), (before_width, after_width))` unique pad widths for each axis.
            - `((before, after),)` yields same before and after pad for height and width.
            - `(pad,)` or int is a shortcut for before = after = pad width for all axes.
        mode (`PaddingMode`):
            The padding mode to use. Can be one of:
                - `"constant"`: pads with a constant value.
                - `"reflect"`: pads with the reflection of the vector mirrored on the first and last values of the
                  vector along each axis.
                - `"replicate"`: pads with the replication of the last value on the edge of the array along each axis.
                - `"symmetric"`: pads with the reflection of the vector mirrored along the edge of the array.
        constant_values (`float` or `Iterable[float]`, *optional*):
            The value to use for the padding if `mode` is `"constant"`.
        data_format (`str` or `ChannelDimension`, *optional*):
            The channel dimension format for the output image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            If unset, will use same as the input image.
        input_data_format (`str` or `ChannelDimension`, *optional*):
            The channel dimension format for the input image. Can be one of:
                - `"channels_first"` or `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `"channels_last"` or `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            If unset, will use the inferred format of the input image.

    Returns:
        `np.ndarray`: The padded image.

    """
    if input_data_format is None:
        input_data_format = infer_channel_dimension_format(image)

    def _expand_for_data_format(values):
        """
        Convert values to be in the format expected by np.pad based on the data format.
        """
        if isinstance(values, (int, float)):
            values = ((values, values), (values, values))
        elif isinstance(values, tuple) and len(values) == 1:
            values = ((values[0], values[0]), (values[0], values[0]))
        elif isinstance(values, tuple) and len(values) == 2 and isinstance(values[0], int):
            values = (values, values)
        elif isinstance(values, tuple) and len(values) == 2 and isinstance(values[0], tuple):
            values = values
        else:
            raise ValueError(f"Unsupported format: {values}")

        # add 0 for channel dimension
        values = ((0, 0), *values) if input_data_format == ChannelDimension.FIRST else (*values, (0, 0))

        # Add additional padding if there's a batch dimension
        values = (0, *values) if image.ndim == 4 else values
        return values

    padding = _expand_for_data_format(padding)

    if mode == PaddingMode.CONSTANT:
        constant_values = _expand_for_data_format(constant_values)
        image = np.pad(image, padding, mode="constant", constant_values=constant_values)
    elif mode == PaddingMode.REFLECT:
        image = np.pad(image, padding, mode="reflect")
    elif mode == PaddingMode.REPLICATE:
        image = np.pad(image, padding, mode="edge")
    elif mode == PaddingMode.SYMMETRIC:
        image = np.pad(image, padding, mode="symmetric")
    else:
        raise ValueError(f"Invalid padding mode: {mode}")

    image = to_channel_dimension_format(image, data_format, input_data_format) if data_format is not None else image
    return image


# TODO (Amy): Accept 1/3/4 channel numpy array as input and return np.array as default
def convert_to_rgb(image: ImageInput) -> ImageInput:
    """
    Converts an image to RGB format. Only converts if the image is of type PIL.Image.Image, otherwise returns the image
    as is.
    Args:
        image (Image):
            The image to convert.
    """
    requires_backends(convert_to_rgb, ["vision"])

    if not isinstance(image, PIL.Image.Image):
        return image

    if image.mode == "RGB":
        return image

    image = image.convert("RGB")
    return image


def flip_channel_order(
    image: np.ndarray,
    data_format: Optional[ChannelDimension] = None,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> np.ndarray:
    """
    Flips the channel order of the image.

    If the image is in RGB format, it will be converted to BGR and vice versa.

    Args:
        image (`np.ndarray`):
            The image to flip.
        data_format (`ChannelDimension`, *optional*):
            The channel dimension format for the output image. Can be one of:
                - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            If unset, will use same as the input image.
        input_data_format (`ChannelDimension`, *optional*):
            The channel dimension format for the input image. Can be one of:
                - `ChannelDimension.FIRST`: image in (num_channels, height, width) format.
                - `ChannelDimension.LAST`: image in (height, width, num_channels) format.
            If unset, will use the inferred format of the input image.
    """
    input_data_format = infer_channel_dimension_format(image) if input_data_format is None else input_data_format

    if input_data_format == ChannelDimension.LAST:
        image = image[..., ::-1]
    elif input_data_format == ChannelDimension.FIRST:
        image = image[::-1, ...]
    else:
        raise ValueError(f"Unsupported channel dimension: {input_data_format}")

    if data_format is not None:
        image = to_channel_dimension_format(image, data_format, input_channel_dim=input_data_format)
    return image


def _cast_tensor_to_float(x):
    if x.is_floating_point():
        return x
    return x.float()


class FusedRescaleNormalize:
    """
    Rescale and normalize the input image in one step.
    """

    def __init__(self, mean, std, rescale_factor: float = 1.0, inplace: bool = False):
        self.mean = torch.tensor(mean) * (1.0 / rescale_factor)
        self.std = torch.tensor(std) * (1.0 / rescale_factor)
        self.inplace = inplace

    def __call__(self, image: "torch.Tensor"):
        image = _cast_tensor_to_float(image)
        return F.normalize(image, self.mean, self.std, inplace=self.inplace)


class Rescale:
    """
    Rescale the input image by rescale factor: image *= rescale_factor.
    """

    def __init__(self, rescale_factor: float = 1.0):
        self.rescale_factor = rescale_factor

    def __call__(self, image: "torch.Tensor"):
        image = image * self.rescale_factor
        return image


class NumpyToTensor:
    """
    Convert a numpy array to a PyTorch tensor.
    """

    def __call__(self, image: np.ndarray):
        # Same as in PyTorch, we assume incoming numpy images are in HWC format
        # c.f. https://github.com/pytorch/vision/blob/61d97f41bc209e1407dcfbd685d2ee2da9c1cdad/torchvision/transforms/functional.py#L154
        return torch.from_numpy(image.transpose(2, 0, 1)).contiguous()
