# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.

from typing import Any, Dict, List, Optional

import torch
from torch import Tensor


def interpolate(
    input: Tensor,
    size: Optional[List[int]] = None,
    scale_factor: Optional[float] = None,
    mode: str = "nearest",
    align_corners: Optional[bool] = None,
) -> Tensor:
    """
    Equivalent to nn.functional.interpolate, but with support for empty channel sizes.
    """
    if input.numel() > 0:
        return torch.nn.functional.interpolate(
            input, size, scale_factor, mode, align_corners
        )

    assert (
        input.shape[0] != 0 or input.shape[1] != 0
    ), "At least one of the two first dimensions must be non zero"

    if input.shape[1] == 0:
        # Pytorch doesn't support null dimension on the channel dimension, so we transpose to fake a null batch dim
        return torch.nn.functional.interpolate(
            input.transpose(0, 1), size, scale_factor, mode, align_corners
        ).transpose(0, 1)

    # empty batch dimension is now supported in pytorch
    return torch.nn.functional.interpolate(
        input, size, scale_factor, mode, align_corners
    )


def targets_to(targets: List[Dict[str, Any]], device):
    """Moves the target dicts to the given device."""
    excluded_keys = [
        "questionId",
        "tokens_positive",
        "tokens",
        "dataset_name",
        "sentence_id",
        "original_img_id",
        "nb_eval",
        "task_id",
        "original_id",
    ]
    return [
        {
            k: v.to(device) if k not in excluded_keys else v
            for k, v in t.items()
            if k != "caption" and k != "answer_type_mask"
        }
        for t in targets
    ]