# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# 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.
"""Image processor class for Fuyu."""

import math
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import (
    pad,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    infer_channel_dimension_format,
    is_scaled_image,
    is_valid_image,
    make_list_of_images,
    to_numpy_array,
    validate_preprocess_arguments,
)
from ...utils import (
    TensorType,
    is_torch_available,
    is_torch_device,
    is_torch_dtype,
    logging,
    requires_backends,
)


if is_torch_available():
    import torch


logger = logging.get_logger(__name__)


def make_list_of_list_of_images(
    images: Union[List[List[ImageInput]], List[ImageInput], ImageInput],
) -> List[List[ImageInput]]:
    if is_valid_image(images):
        return [[images]]

    if isinstance(images, list) and all(isinstance(image, list) for image in images):
        return images

    if isinstance(images, list):
        return [make_list_of_images(image) for image in images]

    raise ValueError("images must be a list of list of images or a list of images or an image.")


class FuyuBatchFeature(BatchFeature):
    """
    BatchFeature class for Fuyu image processor and processor.

    The outputs dictionary from the processors contains a mix of tensors and lists of tensors.
    """

    def convert_to_tensors(self, tensor_type: Optional[Union[str, TensorType]] = None):
        """
        Convert the inner content to tensors.

        Args:
            tensor_type (`str` or [`~utils.TensorType`], *optional*):
                The type of tensors to use. If `str`, should be one of the values of the enum [`~utils.TensorType`]. If
                `None`, no modification is done.
        """
        if tensor_type is None:
            return self

        is_tensor, as_tensor = self._get_is_as_tensor_fns(tensor_type=tensor_type)

        def _convert_tensor(elem):
            if is_tensor(elem):
                return elem
            return as_tensor(elem)

        def _safe_convert_tensor(elem):
            try:
                return _convert_tensor(elem)
            except:  # noqa E722
                if key == "overflowing_values":
                    raise ValueError("Unable to create tensor returning overflowing values of different lengths. ")
                raise ValueError(
                    "Unable to create tensor, you should probably activate padding "
                    "with 'padding=True' to have batched tensors with the same length."
                )

        # Do the tensor conversion in batch
        for key, value in self.items():
            if isinstance(value, list) and isinstance(value[0], list):
                # List[List[Any]] -> List[List[Tensor]]
                self[key] = [[_safe_convert_tensor(elem) for elem in elems] for elems in value]
            elif isinstance(value, list):
                # List[Any] -> List[Tensor]
                self[key] = [_safe_convert_tensor(elem) for elem in value]
            else:
                # Any -> Tensor
                self[key] = _safe_convert_tensor(value)
        return self

    def to(self, *args, **kwargs) -> "BatchFeature":
        """
        Send all values to device by calling `v.to(*args, **kwargs)` (PyTorch only). This should support casting in
        different `dtypes` and sending the `BatchFeature` to a different `device`.

        Args:
            args (`Tuple`):
                Will be passed to the `to(...)` function of the tensors.
            kwargs (`Dict`, *optional*):
                Will be passed to the `to(...)` function of the tensors.

        Returns:
            [`BatchFeature`]: The same instance after modification.
        """
        requires_backends(self, ["torch"])
        import torch  # noqa

        new_data = {}
        device = kwargs.get("device")
        # Check if the args are a device or a dtype
        if device is None and len(args) > 0:
            # device should be always the first argument
            arg = args[0]
            if is_torch_dtype(arg):
                # The first argument is a dtype
                pass
            elif isinstance(arg, str) or is_torch_device(arg) or isinstance(arg, int):
                device = arg
            else:
                # it's something else
                raise ValueError(f"Attempting to cast a BatchFeature to type {str(arg)}. This is not supported.")

        def _to(elem):
            # check if v is a floating point
            if torch.is_floating_point(elem):
                # cast and send to device
                return elem.to(*args, **kwargs)
            if device is not None:
                return elem.to(device=device)

            return elem

        # We cast only floating point tensors to avoid issues with tokenizers casting `LongTensor` to `FloatTensor`
        for k, v in self.items():
            if isinstance(v, list) and isinstance(v[0], list):
                # Data structure is a list of lists
                new_v = []
                for elems in v:
                    new_v.append([_to(elem) for elem in elems])
                new_data[k] = new_v
            elif isinstance(v, list):
                # Data structure is a list
                new_data[k] = [_to(elem) for elem in v]
            else:
                new_data[k] = _to(v)
        self.data = new_data
        return self


class FuyuImageProcessor(BaseImageProcessor):
    """
    This class should handle the image processing part before the main FuyuForCausalLM. In particular, it should
    handle:

    - Processing Images:
        Taking a batch of images as input. If the images are variable-sized, it resizes them based on the desired patch
        dimensions. The image output is always img_h, img_w of (1080, 1920)

        Then, it patches up these images using the patchify_image function.

    - Creating Image Input IDs:
        For each patch, a placeholder ID is given to identify where these patches belong in a token sequence. For
        variable-sized images, each line of patches is terminated with a newline ID.

    - Image Patch Indices:
        For each image patch, the code maintains an index where these patches should be inserted in a token stream.


    Args:
        do_resize (`bool`, *optional*, defaults to `True`):
            Whether to resize the image to `size`.
        size (`Dict[str, int]`, *optional*, defaults to `{"height": 1080, "width": 1920}`):
            Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
        resample (`PILImageResampling`, *optional*, defaults to `Resampling.BILINEAR`):
            `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
        do_pad (`bool`, *optional*, defaults to `True`):
            Whether to pad the image to `size`.
        padding_value (`float`, *optional*, defaults to 1.0):
            The value to pad the image with.
        padding_mode (`str`, *optional*, defaults to `"constant"`):
            The padding mode to use when padding the image.
        do_normalize (`bool`, *optional*, defaults to `True`):
            Whether to normalize the image.
        image_mean (`float`, *optional*, defaults to 0.5):
            The mean to use when normalizing the image.
        image_std (`float`, *optional*, defaults to 0.5):
            The standard deviation to use when normalizing the image.
        do_rescale (`bool`, *optional*, defaults to `True`):
            Whether to rescale the image.
        rescale_factor (`float`, *optional*, defaults to `1 / 255`):
            The factor to use when rescaling the image.
        patch_size (`Dict[str, int]`, *optional*, defaults to `{"height": 30, "width": 30}`):
            Dictionary in the format `{"height": int, "width": int}` specifying the size of the patches.
    """

    model_input_names = [
        "images",
        "image_input_ids",
        "image_patches",
        "image_patch_indices_per_batch",
        "image_patch_indices_per_subsequence",
    ]

    def __init__(
        self,
        do_resize: bool = True,
        size: Optional[Dict[str, int]] = None,
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        do_pad: bool = True,
        padding_value: float = 1.0,
        padding_mode: str = "constant",
        do_normalize: bool = True,
        image_mean: Union[float, List[float]] = 0.5,
        image_std: Union[float, List[float]] = 0.5,
        do_rescale: bool = True,
        rescale_factor: float = 1 / 255,
        patch_size: Optional[Dict[str, int]] = None,
        **kwargs,
    ):
        super().__init__(**kwargs)
        self.do_resize = do_resize
        self.size = size if size is not None else {"height": 1080, "width": 1920}
        self.resample = resample
        self.do_pad = do_pad
        self.padding_value = padding_value
        self.padding_mode = padding_mode
        self.do_normalize = do_normalize
        self.image_mean = image_mean
        self.image_std = image_std
        self.do_rescale = do_rescale
        self.rescale_factor = rescale_factor
        self.patch_size = patch_size if patch_size is not None else {"height": 30, "width": 30}
        self._valid_processor_keys = [
            "images",
            "do_resize",
            "size",
            "resample",
            "do_pad",
            "padding_value",
            "padding_mode",
            "do_normalize",
            "image_mean",
            "image_std",
            "do_rescale",
            "rescale_factor",
            "patch_size",
            "return_tensors",
            "data_format",
            "input_data_format",
        ]

    def resize(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        resample: PILImageResampling = PILImageResampling.BILINEAR,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        **kwargs,
    ) -> np.ndarray:
        """
        Resize an image to `(size["height"], size["width"])`.

        Args:
            image (`np.ndarray`):
                Image to resize.
            size (`Dict[str, int]`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
            resample (`PILImageResampling`, *optional*, defaults to `PILImageResampling.BILINEAR`):
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
            data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the output image. If unset, the channel dimension format of the input
                image is used. 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.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from 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.
                - `"none"` or `ChannelDimension.NONE`: image in (height, width) format.

        Returns:
            `np.ndarray`: The resized image.
        """
        image_height, image_width = get_image_size(image, input_data_format)
        target_height, target_width = size["height"], size["width"]

        if image_width <= target_width and image_height <= target_height:
            return image

        height_scale_factor = target_height / image_height
        width_scale_factor = target_width / image_width
        optimal_scale_factor = min(height_scale_factor, width_scale_factor)

        new_height = int(image_height * optimal_scale_factor)
        new_width = int(image_width * optimal_scale_factor)

        scaled_image = resize(
            image=image,
            size=(new_height, new_width),
            resample=resample,
            data_format=data_format,
            input_data_format=input_data_format,
            **kwargs,
        )
        return scaled_image

    def pad_image(
        self,
        image: np.ndarray,
        size: Dict[str, int],
        mode: str = "constant",
        constant_values: float = 1.0,
        data_format: Optional[Union[str, ChannelDimension]] = None,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
    ) -> np.ndarray:
        """
        Pad an image to `(size["height"], size["width"])`.

        Args:
            image (`np.ndarray`):
                Image to pad.
            size (`Dict[str, int]`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
            data_format (`ChannelDimension` or `str`, *optional*):
                The data format of the output image. If unset, the same format as the input image is used.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format of the input image. If not provided, it will be inferred.
        """
        image_height, image_width = get_image_size(image, input_data_format)
        target_height, target_width = size["height"], size["width"]
        padding_top = 0
        padding_left = 0
        padding_bottom = target_height - image_height
        padding_right = target_width - image_width
        padded_image = pad(
            image,
            padding=((padding_top, padding_bottom), (padding_left, padding_right)),
            mode=mode,
            constant_values=constant_values,
            data_format=data_format,
            input_data_format=input_data_format,
        )
        return padded_image

    def preprocess(
        self,
        images,
        do_resize: Optional[bool] = None,
        size: Optional[Dict[str, int]] = None,
        resample: Optional[PILImageResampling] = None,
        do_pad: Optional[bool] = None,
        padding_value: Optional[float] = None,
        padding_mode: Optional[str] = None,
        do_normalize: Optional[bool] = None,
        image_mean: Optional[float] = None,
        image_std: Optional[float] = None,
        do_rescale: Optional[bool] = None,
        rescale_factor: Optional[float] = None,
        patch_size: Optional[Dict[str, int]] = None,
        data_format: Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST,
        input_data_format: Optional[Union[str, ChannelDimension]] = None,
        return_tensors: Optional[TensorType] = None,
    ):
        """

        Utility function to preprocess the images and extract necessary information about original formats.

        Args:
            images (`ImageInput`):
                Images to preprocess. Expects a single image, a list or images or a list of lists of images. Pixel
                values range from 0 to 255, or between 0 and 1 if `do_rescale` is `False`.
            do_resize (`bool`, *optional*, defaults to `self.do_resize`):
                Whether to resize the image to `size`.
            size (`Dict[str, int]`, *optional*, defaults to `self.size`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the output image.
            resample (`PILImageResampling`, *optional*, defaults to `self.resample`):
                `PILImageResampling` filter to use when resizing the image e.g. `PILImageResampling.BILINEAR`.
            do_pad (`bool`, *optional*, defaults to `self.do_pad`):
                Whether to pad the image to `size`.
            padding_value (`float`, *optional*, defaults to `self.padding_value`):
                The value to pad the image with.
            padding_mode (`str`, *optional*, defaults to `self.padding_mode`):
                The padding mode to use when padding the image.
            do_normalize (`bool`, *optional*, defaults to `self.do_normalize`):
                Whether to normalize the image.
            image_mean (`float`, *optional*, defaults to `self.image_mean`):
                The mean to use when normalizing the image.
            image_std (`float`, *optional*, defaults to `self.image_std`):
                The standard deviation to use when normalizing the image.
            do_rescale (`bool`, *optional*, defaults to `self.do_rescale`):
                Whether to rescale the image.
            rescale_factor (`float`, *optional*, defaults to `self.rescale_factor`):
                The factor to use when rescaling the image.
            patch_size (`Dict[str, int]`, *optional*, defaults to `self.patch_size`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the patches.
            return_tensors (`str` or `TensorType`, *optional*):
                The type of tensors to return. Can be one of:
                - Unset: Return a list of `np.ndarray`.
                - `TensorType.TENSORFLOW` or `'tf'`: Return a batch of type `tf.Tensor`.
                - `TensorType.PYTORCH` or `'pt'`: Return a batch of type `torch.Tensor`.
                - `TensorType.NUMPY` or `'np'`: Return a batch of type `np.ndarray`.
                - `TensorType.JAX` or `'jax'`: Return a batch of type `jax.numpy.ndarray`.
            data_format (`ChannelDimension` or `str`, *optional*, defaults to `ChannelDimension.FIRST`):
                The channel dimension format of 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.
            input_data_format (`ChannelDimension` or `str`, *optional*):
                The channel dimension format for the input image. If unset, the channel dimension format is inferred
                from 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.
        """

        do_resize = do_resize if do_resize is not None else self.do_resize
        size = size if size is not None else self.size
        resample = resample if resample is not None else self.resample
        do_pad = do_pad if do_pad is not None else self.do_pad
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        do_normalize = do_normalize if do_normalize is not None else self.do_normalize
        image_mean = image_mean if image_mean is not None else self.image_mean
        image_std = image_std if image_std is not None else self.image_std
        padding_value = padding_value if padding_value is not None else self.padding_value
        padding_mode = padding_mode if padding_mode is not None else self.padding_mode
        do_rescale = do_rescale if do_rescale is not None else self.do_rescale
        rescale_factor = rescale_factor if rescale_factor is not None else self.rescale_factor
        patch_size = patch_size if patch_size is not None else self.patch_size

        if isinstance(images, list) and any(isinstance(elem, list) and len(elem) >= 2 for elem in images):
            raise ValueError("Multiple images for a single sample are not yet supported.")

        batch_images = make_list_of_list_of_images(images)

        validate_preprocess_arguments(
            do_rescale=do_rescale,
            rescale_factor=rescale_factor,
            do_normalize=do_normalize,
            image_mean=image_mean,
            image_std=image_std,
            do_pad=do_pad,
            size_divisibility=size,  # There is no pad divisibility in this processor, but pad requires the size arg.
            do_resize=do_resize,
            size=size,
            resample=resample,
        )
        # All transformations expect numpy arrays.
        batch_images = [[to_numpy_array(image) for image in images] for images in batch_images]

        if is_scaled_image(batch_images[0][0]) and do_rescale:
            logger.warning_once(
                "It looks like you are trying to rescale already rescaled images. If the input"
                " images have pixel values between 0 and 1, set `do_rescale=False` to avoid rescaling them again."
            )

        if input_data_format is None:
            # We assume that all images have the same channel dimension format.
            input_data_format = infer_channel_dimension_format(batch_images[0][0])

        original_image_sizes = [get_image_size(images[0], channel_dim=input_data_format) for images in batch_images]

        if do_resize:
            batch_images = [
                [self.resize(image, size=size, input_data_format=input_data_format) for image in images]
                for images in batch_images
            ]

        image_sizes = [get_image_size(images[0], channel_dim=input_data_format) for images in batch_images]
        image_unpadded_heights = [[image_size[0]] for image_size in image_sizes]
        image_unpadded_widths = [[image_size[1]] for image_size in image_sizes]

        # scale_h is the same as scale_w
        image_scale_factors = [
            [resized_size[0] / original_size[0]]
            for original_size, resized_size in zip(original_image_sizes, image_sizes)
        ]

        if do_pad:
            batch_images = [
                [
                    self.pad_image(
                        image,
                        size=size,
                        mode=padding_mode,
                        constant_values=padding_value,
                        input_data_format=input_data_format,
                    )
                    for image in images
                ]
                for images in batch_images
            ]

        if do_rescale:
            batch_images = [
                [self.rescale(image, scale=rescale_factor, input_data_format=input_data_format) for image in images]
                for images in batch_images
            ]

        if do_normalize:
            batch_images = [
                [
                    self.normalize(image, mean=image_mean, std=image_std, input_data_format=input_data_format)
                    for image in images
                ]
                for images in batch_images
            ]

        if data_format is not None:
            batch_images = [
                [to_channel_dimension_format(image, data_format, input_data_format) for image in images]
                for images in batch_images
            ]

        data = {
            "images": batch_images,
            "image_unpadded_heights": image_unpadded_heights,
            "image_unpadded_widths": image_unpadded_widths,
            "image_scale_factors": image_scale_factors,
        }
        return FuyuBatchFeature(data=data, tensor_type=return_tensors)

    def get_num_patches(self, image_height: int, image_width: int, patch_size: Dict[str, int] = None) -> int:
        """
        Calculate number of patches required to encode an image.

        Args:
            image_height (`int`):
                Height of the image.
            image_width (`int`):
                Width of the image.
            patch_size (`Dict[str, int]`, *optional*, defaults to `self.patch_size`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the patches.
        """
        patch_size = patch_size if patch_size is not None else self.patch_size
        patch_height, patch_width = self.patch_size["height"], self.patch_size["width"]

        if image_height % patch_height != 0:
            raise ValueError(f"{image_height=} must be divisible by {patch_height}")
        if image_width % patch_width != 0:
            raise ValueError(f"{image_width=} must be divisible by {patch_width}")

        num_patches_per_dim_h = image_height // patch_height
        num_patches_per_dim_w = image_width // patch_width
        num_patches = num_patches_per_dim_h * num_patches_per_dim_w
        return num_patches

    def patchify_image(self, image: "torch.Tensor", patch_size: Optional[Dict[str, int]] = None) -> "torch.Tensor":
        """
        Convert an image into a tensor of patches.

        Args:
            image (`torch.Tensor`):
                Image to convert. Shape: [batch, channels, height, width]
            patch_size (`Dict[str, int]`, *optional*, defaults to `self.patch_size`):
                Dictionary in the format `{"height": int, "width": int}` specifying the size of the patches.
        """
        requires_backends(self, ["torch"])
        patch_size = patch_size if patch_size is not None else self.patch_size
        patch_height, patch_width = patch_size["height"], patch_size["width"]

        # TODO refer to https://github.com/ArthurZucker/transformers/blob/0f0a3fe5ca5697ee58faeb5b53f049af720b5e98/src/transformers/models/vit_mae/modeling_vit_mae.py#L871
        # torch implementation is faster but does not handle non-squares

        batch_size, channels, _, _ = image.shape
        unfolded_along_height = image.unfold(2, patch_height, patch_height)
        patches = unfolded_along_height.unfold(3, patch_width, patch_width)
        patches = patches.contiguous()
        patches = patches.view(batch_size, channels, -1, patch_height, patch_width)
        patches = patches.permute(0, 2, 3, 4, 1)
        patches = patches.reshape(batch_size, -1, channels * patch_height * patch_width)
        return patches

    def preprocess_with_tokenizer_info(
        self,
        image_input: "torch.Tensor",
        image_present: "torch.Tensor",
        image_unpadded_h: "torch.Tensor",
        image_unpadded_w: "torch.Tensor",
        image_placeholder_id: int,
        image_newline_id: int,
        variable_sized: bool,
        patch_size: Optional[Dict[str, int]] = None,
    ) -> FuyuBatchFeature:
        """Process images for model input. In particular, variable-sized images are handled here.

        Args:
            image_input (`torch.Tensor` of shape [batch_size, subsequence_size, num_channels, height, width]):
                Tensor of images padded to model input size.
            image_present (`torch.Tensor` of shape [batch_size, subsequence_size, num_images]):
                Tensor of 1s and 0s indicating whether an image is present.
            image_unpadded_h (`torch.Tensor` of shape [batch_size, subsequence_size]):
                Tensor of unpadded image heights.
            image_unpadded_w (`torch.Tensor` of shape [batch_size, subsequence_size]):
                Tensor of unpadded image widths.
            image_placeholder_id (int):
                The id of the image placeholder token. Comes from an associated tokenizer.
            image_newline_id (int):
                The id of the image newline token. Comes from an associated tokenizer.
            variable_sized (bool):
                Whether to process images as variable-sized.
            patch_size (`Dict[str, int]`, *optional*, defaults to `self.patch_size`):
                Size of the patches.
        """
        requires_backends(self, ["torch"])

        patch_size = patch_size if patch_size is not None else self.patch_size
        patch_height, patch_width = patch_size["height"], patch_size["width"]

        # Only images that are present.
        images: List[List[torch.Tensor]] = []
        batch_image_patches: List[List[torch.Tensor]] = []
        # Image input ids for every subsequence, including ones with no image present.
        batch_image_input_ids: List[List[torch.Tensor]] = []
        for batch_index in range(image_input.shape[0]):
            image_input_ids = []
            image_patches = []
            for subseq_index in range(image_input.shape[1]):
                if image_present[batch_index, subseq_index]:
                    image = image_input[batch_index, subseq_index]
                    image_height, image_width = image.shape[1], image.shape[2]
                    if variable_sized:
                        # The min() is required here due to floating point issues:
                        # math.ceil(torch.tensor(300).cuda() / 30) == 11
                        new_h = min(
                            image_height,
                            math.ceil(image_unpadded_h[batch_index, subseq_index] / patch_height) * patch_height,
                        )
                        new_w = min(
                            image_width,
                            math.ceil(image_unpadded_w[batch_index, subseq_index] / patch_width) * patch_width,
                        )
                        image = image[:, :new_h, :new_w]
                        image_height, image_width = new_h, new_w

                    num_patches = self.get_num_patches(image_height=image_height, image_width=image_width)
                    tensor_of_image_ids = torch.full(
                        [num_patches], image_placeholder_id, dtype=torch.int32, device=image_input.device
                    )
                    patches = self.patchify_image(image=image.unsqueeze(0)).squeeze(0)
                    assert num_patches == patches.shape[0]

                    if variable_sized:
                        # Now terminate each line with |NEWLINE|.
                        tensor_of_image_ids = tensor_of_image_ids.reshape(-1, image_width // patch_width)
                        newline_ids = torch.full(
                            [tensor_of_image_ids.shape[0], 1],
                            image_newline_id,
                            dtype=torch.int32,
                            device=image_input.device,
                        )
                        tensor_of_image_ids = torch.cat([tensor_of_image_ids, newline_ids], dim=1)
                        tensor_of_image_ids = tensor_of_image_ids.reshape(-1)

                    images.append([image])
                    image_input_ids.append(tensor_of_image_ids)
                    image_patches.append(patches)
                else:
                    image_input_ids.append(torch.tensor([], dtype=torch.int32, device=image_input.device))

            batch_image_input_ids.append(image_input_ids)
            batch_image_patches.append(image_patches)

        # Create image_patch_input_indices, where non-negative values correspond to image patches to be inserted in
        # the stream.
        image_patch_indices_per_batch: List[List[torch.Tensor]] = []
        image_patch_indices_per_subsequence: List[List[torch.Tensor]] = []

        for sample_image_input_ids in batch_image_input_ids:
            index_offset = 0
            per_batch_indices = []
            per_subsequence_indices = []
            for subseq_image_input_ids in sample_image_input_ids:
                # Indices of image patches.
                patches_mask = subseq_image_input_ids == image_placeholder_id
                num_patches = torch.count_nonzero(patches_mask)
                indices = torch.arange(num_patches, dtype=torch.int64, device=subseq_image_input_ids.device).type_as(
                    subseq_image_input_ids
                )

                # Place those indices in the image input ids token stream, with -1 representing non-index tokens.
                indices_in_stream_per_batch = torch.full_like(subseq_image_input_ids, -1)
                indices_in_stream_per_subsequence = torch.full_like(subseq_image_input_ids, -1)
                patches_inds = torch.nonzero(patches_mask, as_tuple=True)[0]

                indices_in_stream_per_batch[patches_inds] = indices + index_offset
                indices_in_stream_per_subsequence[patches_inds] = indices

                per_batch_indices.append(indices_in_stream_per_batch)
                per_subsequence_indices.append(indices_in_stream_per_subsequence)
                index_offset += num_patches

            image_patch_indices_per_batch.append(per_batch_indices)
            image_patch_indices_per_subsequence.append(per_subsequence_indices)

        return FuyuBatchFeature(
            data={
                "images": images,
                "image_input_ids": batch_image_input_ids,
                "image_patches": batch_image_patches,
                "image_patch_indices_per_batch": image_patch_indices_per_batch,
                "image_patch_indices_per_subsequence": image_patch_indices_per_subsequence,
            }
        )
