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# Source code for torchvision.utils

    import collections
    import math
    import pathlib
    import warnings
    from itertools import repeat
    from types import FunctionType
    from typing import Any, BinaryIO, List, Optional, Tuple, Union

    import numpy as np
    import torch
    from PIL import Image, ImageColor, ImageDraw, ImageFont

    __all__ = [
        "make_grid",
        "save_image",
        "draw_bounding_boxes",
        "draw_segmentation_masks",
        "draw_keypoints",
        "flow_to_image",
    ]

    @torch.no_grad()
    def make_grid(
        tensor: Union[torch.Tensor, List[torch.Tensor]],
        nrow: int = 8,
        padding: int = 2,
        normalize: bool = False,
        value_range: Optional[Tuple[int, int]] = None,
        scale_each: bool = False,
        pad_value: float = 0.0,
    ) -> torch.Tensor:
        """
        Make a grid of images.

        Args:
            tensor (Tensor or list): 4D mini-batch Tensor of shape (B x C x H x W)
                or a list of images all of the same size.
            nrow (int, optional): Number of images displayed in each row of the grid.
                The final grid size is ``(B / nrow, nrow)``. Default: ``8``.
            padding (int, optional): amount of padding. Default: ``2``.
            normalize (bool, optional): If True, shift the image to the range (0, 1),
                by the min and max values specified by ``value_range``. Default: ``False``.
            value_range (tuple, optional): tuple (min, max) where min and max are numbers,
                then these numbers are used to normalize the image. By default, min and max
                are computed from the tensor.
            scale_each (bool, optional): If ``True``, scale each image in the batch of
                images separately rather than the (min, max) over all images. Default: ``False``.
            pad_value (float, optional): Value for the padded pixels. Default: ``0``.

        Returns:
            grid (Tensor): the tensor containing grid of images.
        """
        if not torch.jit.is_scripting() and not torch.jit.is_tracing():
            _log_api_usage_once(make_grid)
        if not torch.is_tensor(tensor):
            if isinstance(tensor, list):
                for t in tensor:
                    if not torch.is_tensor(t):
                        raise TypeError(f"tensor or list of tensors expected, got a list containing {type(t)}")
            else:
                raise TypeError(f"tensor or list of tensors expected, got {type(tensor)}")

        # if list of tensors, convert to a 4D mini-batch Tensor
        if isinstance(tensor, list):
            tensor = torch.stack(tensor, dim=0)

        if tensor.dim() == 2:  # single image H x W
            tensor = tensor.unsqueeze(0)
        if tensor.dim() == 3:  # single image
            if tensor.size(0) == 1:  # if single-channel, convert to 3-channel
                tensor = torch.cat((tensor, tensor, tensor), 0)
            tensor = tensor.unsqueeze(0)

        if tensor.dim() == 4 and tensor.size(1) == 1:  # single-channel images
            tensor = torch.cat((tensor, tensor, tensor), 1)

        if normalize is True:
            tensor = tensor.clone()  # avoid modifying tensor in-place
            if value_range is not None and not isinstance(value_range, tuple):
                raise TypeError("value_range has to be a tuple (min, max) if specified. min and max are numbers")

            def norm_ip(img, low, high):
                img.clamp_(min=low, max=high)
                img.sub_(low).div_(max(high - low, 1e-5))

            def norm_range(t, value_range):
                if value_range is not None:
                    norm_ip(t, value_range[0], value_range[1])
                else:
                    norm_ip(t, float(t.min()), float(t.max()))

            if scale_each is True:
                for t in tensor:  # loop over mini-batch dimension
                    norm_range(t, value_range)
            else:
                norm_range(tensor, value_range)

        if not isinstance(tensor, torch.Tensor):
            raise TypeError("tensor should be of type torch.Tensor")
        if tensor.size(0) == 1:
            return tensor.squeeze(0)

        # make the mini-batch of images into a grid
        nmaps = tensor.size(0)
        xmaps = min(nrow, nmaps)
        ymaps = int(math.ceil(float(nmaps) / xmaps))
        height, width = int(tensor.size(2) + padding), int(tensor.size(3) + padding)
        num_channels = tensor.size(1)
        grid = tensor.new_full((num_channels, height * ymaps + padding, width * xmaps + padding), pad_value)
        k = 0
        for y in range(ymaps):
            for x in range(xmaps):
                if k >= nmaps:
                    break
                # Tensor.copy_() is a valid method but seems to be missing from the stubs
                # https://pytorch.org/docs/stable/tensors.html#torch.Tensor.copy_
                grid.narrow(1, y * height + padding, height - padding).narrow(  # type: ignore[attr-defined]
                    2, x * width + padding, width - padding
                ).copy_(tensor[k])
                k = k + 1
        return grid

    @torch.no_grad()
    def save_image(
        tensor: Union[torch.Tensor, List[torch.Tensor]],
        fp: Union[str, pathlib.Path, BinaryIO],
        format: Optional[str] = None,
        **kwargs,
    ) -> None:
        """
        Save a given Tensor into an image file.

        Args:
            tensor (Tensor or list): Image to be saved. If given a mini-batch tensor,
                saves the tensor as a grid of images by calling ``make_grid``.
            fp (string or file object): A filename or a file object
            format(Optional):  If omitted, the format to use is determined from the filename extension.
                If a file object was used instead of a filename, this parameter should always be used.
            **kwargs: Other arguments are documented in ``make_grid``.
        """

        if not torch.jit.is_scripting() and not torch.jit.is_tracing():
            _log_api_usage_once(save_image)
        grid = make_grid(tensor, **kwargs)
        # Add 0.5 after unnormalizing to [0, 255] to round to the nearest integer
        ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to("cpu", torch.uint8).numpy()
        im = Image.fromarray(ndarr)
        im.save(fp, format=format)

    @torch.no_grad()
    def draw_bounding_boxes(
        image: torch.Tensor,
        boxes: torch.Tensor,
        labels: Optional[List[str]] = None,
        colors: Optional[Union[List[Union[str, Tuple[int, int, int]]], str, Tuple[int, int, int]]] = None,
        fill: Optional[bool] = False,
        width: int = 1,
        font: Optional[str] = None,
        font_size: Optional[int] = None,
    ) -> torch.Tensor:

        """
        Draws bounding boxes on given image.
        The values of the input image should be uint8 between 0 and 255.
        If fill is True, Resulting Tensor should be saved as PNG image.

        Args:
            image (Tensor): Tensor of shape (C x H x W) and dtype uint8.
            boxes (Tensor): Tensor of size (N, 4) containing bounding boxes in (xmin, ymin, xmax, ymax) format. Note that
                the boxes are absolute coordinates with respect to the image. In other words: `0 <= xmin < xmax < W` and
                `0 <= ymin < ymax < H`.
            labels (List[str]): List containing the labels of bounding boxes.
            colors (color or list of colors, optional): List containing the colors
                of the boxes or single color for all boxes. The color can be represented as
                PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.
                By default, random colors are generated for boxes.
            fill (bool): If `True` fills the bounding box with specified color.
            width (int): Width of bounding box.
            font (str): A filename containing a TrueType font. If the file is not found in this filename, the loader may
                also search in other directories, such as the `fonts/` directory on Windows or `/Library/Fonts/`,
                `/System/Library/Fonts/` and `~/Library/Fonts/` on macOS.
            font_size (int): The requested font size in points.

        Returns:
            img (Tensor[C, H, W]): Image Tensor of dtype uint8 with bounding boxes plotted.
        """

        if not torch.jit.is_scripting() and not torch.jit.is_tracing():
            _log_api_usage_once(draw_bounding_boxes)
        if not isinstance(image, torch.Tensor):
            raise TypeError(f"Tensor expected, got {type(image)}")
        elif image.dtype != torch.uint8:
            raise ValueError(f"Tensor uint8 expected, got {image.dtype}")
        elif image.dim() != 3:
            raise ValueError("Pass individual images, not batches")
        elif image.size(0) not in {1, 3}:
            raise ValueError("Only grayscale and RGB images are supported")
        elif (boxes[:, 0] > boxes[:, 2]).any() or (boxes[:, 1] > boxes[:, 3]).any():
            raise ValueError(
                "Boxes need to be in (xmin, ymin, xmax, ymax) format. Use torchvision.ops.box_convert to convert them"
            )

        num_boxes = boxes.shape[0]

        if num_boxes == 0:
            warnings.warn("boxes doesn't contain any box. No box was drawn")
            return image

        if labels is None:
            labels: Union[List[str], List[None]] = [None] * num_boxes  # type: ignore[no-redef]
        elif len(labels) != num_boxes:
            raise ValueError(
                f"Number of boxes ({num_boxes}) and labels ({len(labels)}) mismatch. Please specify labels for each box."
            )

        colors = _parse_colors(colors, num_objects=num_boxes)

        if font is None:
            if font_size is not None:
                warnings.warn("Argument 'font_size' will be ignored since 'font' is not set.")
            txt_font = ImageFont.load_default()
        else:
            txt_font = ImageFont.truetype(font=font, size=font_size or 10)

        # Handle Grayscale images
        if image.size(0) == 1:
            image = torch.tile(image, (3, 1, 1))

        ndarr = image.permute(1, 2, 0).cpu().numpy()
        img_to_draw = Image.fromarray(ndarr)
        img_boxes = boxes.to(torch.int64).tolist()

        if fill:
            draw = ImageDraw.Draw(img_to_draw, "RGBA")
        else:
            draw = ImageDraw.Draw(img_to_draw)

        for bbox, color, label in zip(img_boxes, colors, labels):  # type: ignore[arg-type]
            if fill:
                fill_color = color + (100,)
                draw.rectangle(bbox, width=width, outline=color, fill=fill_color)
            else:
                draw.rectangle(bbox, width=width, outline=color)

            if label is not None:
                margin = width + 1
                draw.text((bbox[0] + margin, bbox[1] + margin), label, fill=color, font=txt_font)

        return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1).to(dtype=torch.uint8)

    @torch.no_grad()
    def draw_segmentation_masks(
        image: torch.Tensor,
        masks: torch.Tensor,
        alpha: float = 0.8,
        colors: Optional[Union[List[Union[str, Tuple[int, int, int]]], str, Tuple[int, int, int]]] = None,
    ) -> torch.Tensor:

        """
        Draws segmentation masks on given RGB image.
        The values of the input image should be uint8 between 0 and 255.

        Args:
            image (Tensor): Tensor of shape (3, H, W) and dtype uint8.
            masks (Tensor): Tensor of shape (num_masks, H, W) or (H, W) and dtype bool.
            alpha (float): Float number between 0 and 1 denoting the transparency of the masks.
                0 means full transparency, 1 means no transparency.
            colors (color or list of colors, optional): List containing the colors
                of the masks or single color for all masks. The color can be represented as
                PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.
                By default, random colors are generated for each mask.

        Returns:
            img (Tensor[C, H, W]): Image Tensor, with segmentation masks drawn on top.
        """

        if not torch.jit.is_scripting() and not torch.jit.is_tracing():
            _log_api_usage_once(draw_segmentation_masks)
        if not isinstance(image, torch.Tensor):
            raise TypeError(f"The image must be a tensor, got {type(image)}")
        elif image.dtype != torch.uint8:
            raise ValueError(f"The image dtype must be uint8, got {image.dtype}")
        elif image.dim() != 3:
            raise ValueError("Pass individual images, not batches")
        elif image.size()[0] != 3:
            raise ValueError("Pass an RGB image. Other Image formats are not supported")
        if masks.ndim == 2:
            masks = masks[None, :, :]
        if masks.ndim != 3:
            raise ValueError("masks must be of shape (H, W) or (batch_size, H, W)")
        if masks.dtype != torch.bool:
            raise ValueError(f"The masks must be of dtype bool. Got {masks.dtype}")
        if masks.shape[-2:] != image.shape[-2:]:
            raise ValueError("The image and the masks must have the same height and width")

        num_masks = masks.size()[0]

        if num_masks == 0:
            warnings.warn("masks doesn't contain any mask. No mask was drawn")
            return image

        out_dtype = torch.uint8
        colors = [
            torch.tensor(color, dtype=out_dtype, device=image.device)
            for color in _parse_colors(colors, num_objects=num_masks)
        ]

        img_to_draw = image.detach().clone()
        # TODO: There might be a way to vectorize this
        for mask, color in zip(masks, colors):
            img_to_draw[:, mask] = color[:, None]

        out = image * (1 - alpha) + img_to_draw * alpha
        return out.to(out_dtype)

    @torch.no_grad()
    def draw_keypoints(
        image: torch.Tensor,
        keypoints: torch.Tensor,
        connectivity: Optional[List[Tuple[int, int]]] = None,
        colors: Optional[Union[str, Tuple[int, int, int]]] = None,
        radius: int = 2,
        width: int = 3,
    ) -> torch.Tensor:

        """
        Draws Keypoints on given RGB image.
        The values of the input image should be uint8 between 0 and 255.

        Args:
            image (Tensor): Tensor of shape (3, H, W) and dtype uint8.
            keypoints (Tensor): Tensor of shape (num_instances, K, 2) the K keypoints location for each of the N instances,
                in the format [x, y].
            connectivity (List[Tuple[int, int]]]): A List of tuple where,
                each tuple contains pair of keypoints to be connected.
            colors (str, Tuple): The color can be represented as
                PIL strings e.g. "red" or "#FF00FF", or as RGB tuples e.g. ``(240, 10, 157)``.
            radius (int): Integer denoting radius of keypoint.
            width (int): Integer denoting width of line connecting keypoints.

        Returns:
            img (Tensor[C, H, W]): Image Tensor of dtype uint8 with keypoints drawn.
        """

        if not torch.jit.is_scripting() and not torch.jit.is_tracing():
            _log_api_usage_once(draw_keypoints)
        if not isinstance(image, torch.Tensor):
            raise TypeError(f"The image must be a tensor, got {type(image)}")
        elif image.dtype != torch.uint8:
            raise ValueError(f"The image dtype must be uint8, got {image.dtype}")
        elif image.dim() != 3:
            raise ValueError("Pass individual images, not batches")
        elif image.size()[0] != 3:
            raise ValueError("Pass an RGB image. Other Image formats are not supported")

        if keypoints.ndim != 3:
            raise ValueError("keypoints must be of shape (num_instances, K, 2)")

        ndarr = image.permute(1, 2, 0).cpu().numpy()
        img_to_draw = Image.fromarray(ndarr)
        draw = ImageDraw.Draw(img_to_draw)
        img_kpts = keypoints.to(torch.int64).tolist()

        for kpt_id, kpt_inst in enumerate(img_kpts):
            for inst_id, kpt in enumerate(kpt_inst):
                x1 = kpt[0] - radius
                x2 = kpt[0] + radius
                y1 = kpt[1] - radius
                y2 = kpt[1] + radius
                draw.ellipse([x1, y1, x2, y2], fill=colors, outline=None, width=0)

            if connectivity:
                for connection in connectivity:
                    start_pt_x = kpt_inst[connection[0]][0]
                    start_pt_y = kpt_inst[connection[0]][1]

                    end_pt_x = kpt_inst[connection[1]][0]
                    end_pt_y = kpt_inst[connection[1]][1]

                    draw.line(
                        ((start_pt_x, start_pt_y), (end_pt_x, end_pt_y)),
                        width=width,
                    )

        return torch.from_numpy(np.array(img_to_draw)).permute(2, 0, 1).to(dtype=torch.uint8)

    # Flow visualization code adapted from https://github.com/tomrunia/OpticalFlow_Visualization
    @torch.no_grad()
    def flow_to_image(flow: torch.Tensor) -> torch.Tensor:

        """
        Converts a flow to an RGB image.

        Args:
            flow (Tensor): Flow of shape (N, 2, H, W) or (2, H, W) and dtype torch.float.

        Returns:
            img (Tensor): Image Tensor of dtype uint8 where each color corresponds
                to a given flow direction. Shape is (N, 3, H, W) or (3, H, W) depending on the input.
        """

        if flow.dtype != torch.float:
            raise ValueError(f"Flow should be of dtype torch.float, got {flow.dtype}.")

        orig_shape = flow.shape
        if flow.ndim == 3:
            flow = flow[None]  # Add batch dim

        if flow.ndim != 4 or flow.shape[1] != 2:
            raise ValueError(f"Input flow should have shape (2, H, W) or (N, 2, H, W), got {orig_shape}.")

        max_norm = torch.sum(flow**2, dim=1).sqrt().max()
        epsilon = torch.finfo((flow).dtype).eps
        normalized_flow = flow / (max_norm + epsilon)
        img = _normalized_flow_to_image(normalized_flow)

        if len(orig_shape) == 3:
            img = img[0]  # Remove batch dim
        return img

    @torch.no_grad()
    def _normalized_flow_to_image(normalized_flow: torch.Tensor) -> torch.Tensor:

        """
        Converts a batch of normalized flow to an RGB image.

        Args:
            normalized_flow (torch.Tensor): Normalized flow tensor of shape (N, 2, H, W)
        Returns:
           img (Tensor(N, 3, H, W)): Flow visualization image of dtype uint8.
        """

        N, _, H, W = normalized_flow.shape
        device = normalized_flow.device
        flow_image = torch.zeros((N, 3, H, W), dtype=torch.uint8, device=device)
        colorwheel = _make_colorwheel().to(device)  # shape [55x3]
        num_cols = colorwheel.shape[0]
        norm = torch.sum(normalized_flow**2, dim=1).sqrt()
        a = torch.atan2(-normalized_flow[:, 1, :, :], -normalized_flow[:, 0, :, :]) / torch.pi
        fk = (a + 1) / 2 * (num_cols - 1)
        k0 = torch.floor(fk).to(torch.long)
        k1 = k0 + 1
        k1[k1 == num_cols] = 0
        f = fk - k0

        for c in range(colorwheel.shape[1]):
            tmp = colorwheel[:, c]
            col0 = tmp[k0] / 255.0
            col1 = tmp[k1] / 255.0
            col = (1 - f) * col0 + f * col1
            col = 1 - norm * (1 - col)
            flow_image[:, c, :, :] = torch.floor(255 * col)
        return flow_image

    def _make_colorwheel() -> torch.Tensor:
        """
        Generates a color wheel for optical flow visualization as presented in:
        Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007)
        URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf.

        Returns:
            colorwheel (Tensor[55, 3]): Colorwheel Tensor.
        """

        RY = 15
        YG = 6
        GC = 4
        CB = 11
        BM = 13
        MR = 6

        ncols = RY + YG + GC + CB + BM + MR
        colorwheel = torch.zeros((ncols, 3))
        col = 0

        # RY
        colorwheel[0:RY, 0] = 255
        colorwheel[0:RY, 1] = torch.floor(255 * torch.arange(0, RY) / RY)
        col = col + RY
        # YG
        colorwheel[col : col + YG, 0] = 255 - torch.floor(255 * torch.arange(0, YG) / YG)
        colorwheel[col : col + YG, 1] = 255
        col = col + YG
        # GC
        colorwheel[col : col + GC, 1] = 255
        colorwheel[col : col + GC, 2] = torch.floor(255 * torch.arange(0, GC) / GC)
        col = col + GC
        # CB
        colorwheel[col : col + CB, 1] = 255 - torch.floor(255 * torch.arange(CB) / CB)
        colorwheel[col : col + CB, 2] = 255
        col = col + CB
        # BM
        colorwheel[col : col + BM, 2] = 255
        colorwheel[col : col + BM, 0] = torch.floor(255 * torch.arange(0, BM) / BM)
        col = col + BM
        # MR
        colorwheel[col : col + MR, 2] = 255 - torch.floor(255 * torch.arange(MR) / MR)
        colorwheel[col : col + MR, 0] = 255
        return colorwheel

    def _generate_color_palette(num_objects: int):
        palette = torch.tensor([2**25 - 1, 2**15 - 1, 2**21 - 1])
        return [tuple((i * palette) % 255) for i in range(num_objects)]

    def _parse_colors(
        colors: Union[None, str, Tuple[int, int, int], List[Union[str, Tuple[int, int, int]]]],
        *,
        num_objects: int,
    ) -> List[Tuple[int, int, int]]:
        """
        Parses a specification of colors for a set of objects.

        Args:
            colors: A specification of colors for the objects. This can be one of the following:
                - None: to generate a color palette automatically.
                - A list of colors: where each color is either a string (specifying a named color) or an RGB tuple.
                - A string or an RGB tuple: to use the same color for all objects.

                If `colors` is a tuple, it should be a 3-tuple specifying the RGB values of the color.
                If `colors` is a list, it should have at least as many elements as the number of objects to color.

            num_objects (int): The number of objects to color.

        Returns:
            A list of 3-tuples, specifying the RGB values of the colors.

        Raises:
            ValueError: If the number of colors in the list is less than the number of objects to color.
                        If `colors` is not a list, tuple, string or None.
        """
        if colors is None:
            colors = _generate_color_palette(num_objects)
        elif isinstance(colors, list):
            if len(colors) < num_objects:
                raise ValueError(
                    f"Number of colors must be equal or larger than the number of objects, but got {len(colors)} < {num_objects}."
                )
        elif not isinstance(colors, (tuple, str)):
            raise ValueError("`colors` must be a tuple or a string, or a list thereof, but got {colors}.")
        elif isinstance(colors, tuple) and len(colors) != 3:
            raise ValueError("If passed as tuple, colors should be an RGB triplet, but got {colors}.")
        else:  # colors specifies a single color for all objects
            colors = [colors] * num_objects

        return [ImageColor.getrgb(color) if isinstance(color, str) else color for color in colors]

    def _log_api_usage_once(obj: Any) -> None:

        """
        Logs API usage(module and name) within an organization.
        In a large ecosystem, it's often useful to track the PyTorch and
        TorchVision APIs usage. This API provides the similar functionality to the
        logging module in the Python stdlib. It can be used for debugging purpose
        to log which methods are used and by default it is inactive, unless the user
        manually subscribes a logger via the `SetAPIUsageLogger method <https://github.com/pytorch/pytorch/blob/eb3b9fe719b21fae13c7a7cf3253f970290a573e/c10/util/Logging.cpp#L114>`_.
        Please note it is triggered only once for the same API call within a process.
        It does not collect any data from open-source users since it is no-op by default.
        For more information, please refer to
        * PyTorch note: https://pytorch.org/docs/stable/notes/large_scale_deployments.html#api-usage-logging;
        * Logging policy: https://github.com/pytorch/vision/issues/5052;

        Args:
            obj (class instance or method): an object to extract info from.
        """
        module = obj.__module__
        if not module.startswith("torchvision"):
            module = f"torchvision.internal.{module}"
        name = obj.__class__.__name__
        if isinstance(obj, FunctionType):
            name = obj.__name__
        torch._C._log_api_usage_once(f"{module}.{name}")

    def _make_ntuple(x: Any, n: int) -> Tuple[Any, ...]:
        """
        Make n-tuple from input x. If x is an iterable, then we just convert it to tuple.
        Otherwise, we will make a tuple of length n, all with value of x.
        reference: https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/utils.py#L8

        Args:
            x (Any): input value
            n (int): length of the resulting tuple
        """
        if isinstance(x, collections.abc.Iterable):
            return tuple(x)
        return tuple(repeat(x, n))

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