from __future__ import annotations

import torch


def sparse_allclose(
    input: torch.Tensor, other: torch.Tensor, rtol: float = 1e-05, atol: float = 1e-08, equal_nan: bool = False
) -> bool:
    """
    Check if two sparse embeddings are close to each other.

    This function works with sparse embeddings in either:
    1. Tensor format (assuming sparse tensors)
    2. Dictionary format with 'indices' and 'values' keys

    Args:
        input: First sparse embedding (tensor)
        other: Second sparse embedding (tensor)
        rtol: Relative tolerance
        atol: Absolute tolerance
        equal_nan: If True, NaN values in the same locations are considered equal

    Returns:
        bool: True if embeddings are close according to tolerances
    """
    # Check if shape matches
    if input.shape != other.shape:
        return False

    input = input.coalesce()
    other = other.coalesce()

    # Convert dict format to appropriate tensors if needed
    input_indices = input.indices()
    input_values = input.values()

    other_indices = other.indices()
    other_values = other.values()

    # Check if indices are the same
    if not torch.equal(input_indices, other_indices):
        return False

    # Check if values are close
    return torch.allclose(input_values, other_values, rtol=rtol, atol=atol, equal_nan=equal_nan)