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#
# Licensed under the Apache License, Version 2.0 (the "License");
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#     http://www.apache.org/licenses/LICENSE-2.0
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from __future__ import annotations

from typing import TYPE_CHECKING

__all__ = []

from paddle import _C_ops
from paddle.base.framework import in_dynamic_or_pir_mode

if TYPE_CHECKING:
    from paddle import Tensor


def attention(
    query: Tensor,
    key: Tensor,
    value: Tensor,
    sparse_mask: Tensor,
    key_padding_mask: Tensor | None = None,
    attn_mask: Tensor | None = None,
    name: str | None = None,
) -> Tensor:
    r"""
    Note:
        This API is only used from ``CUDA 11.8`` .

    SparseCsrTensor is used to store the intermediate result of Attention matrix
    in Transformer module, which can reduce memory usage and improve performance.
    ``sparse_mask`` express the sparse layout in CSR format.
    The calculation equation is:

    .. math::

        result = softmax(\frac{ Q * K^T }{\sqrt{d}}) * V

    where : ``Q``, ``K``, and ``V`` represent the three input parameters of the attention module.
    The shape of the three parameters are: `[batch_size, num_heads, seq_len, head_dim]`, and
    ``d`` represents ``head_dim`` .

    Args:
        query (DenseTensor): `query` in the Attention module. 4D Tensor with float32 or float64.
        key (DenseTensor): `key` in the Attention module. 4D Tensor with float32 or float64.
        value (DenseTensor): `value` in the Attention module. 4D Tensor with float32 or float64.
        sparse_mask (SparseCsrTensor): The sparse layout in the Attention module. Its dense shape
            is `[batch_size*num_heads, seq_len, seq_len]`. `nnz` of each batch must be the same.
            dtype of `crows` and `cols` must be int64, dtype of `values` can be float32 or float64.
        key_padding_mask (DenseTensor|None, optional): The key padding mask tensor in the Attention module.
            2D tensor with shape: [batch_size, seq_len]. dtype can be float32 or float64. Default: None.
        attn_mask (DenseTensor|None, optional): The attention mask tensor in the Attention module.
            2D tensor with shape: [seq_len, seq_len]. dtype can be float32 or float64. Default: None.
        name (str|None, optional): The default value is None. Normally there is no need for user
            to set this property. For more information, please refer to :ref:`api_guide_Name`.

    Returns:
        4D tensor with shape: [batch_size, num_heads, seq_len, head_dim]. dtype is same with input.

    Examples:
        .. code-block:: python

            >>> # doctest: +REQUIRES(env:GPU)
            >>> import paddle
            >>> paddle.device.set_device('gpu')

            >>> batch_size = 16
            >>> num_heads = 16
            >>> seq_len = 512
            >>> head_dim = 32

            >>> query = paddle.rand([batch_size, num_heads, seq_len, head_dim])
            >>> key = paddle.rand([batch_size, num_heads, seq_len, head_dim])
            >>> value = paddle.rand([batch_size, num_heads, seq_len, head_dim])

            >>> query.stop_gradient = False
            >>> key.stop_gradient = False
            >>> value.stop_gradient = False

            >>> mask = paddle.nn.functional.dropout(paddle.ones([seq_len, seq_len])).expand([batch_size, num_heads, seq_len, seq_len])
            >>> sp_mask = mask.reshape([-1, seq_len, seq_len]).to_sparse_csr()

            >>> kp_mask = paddle.randint(0, 2, [batch_size, seq_len]).astype(paddle.float32)
            >>> attn_mask = paddle.randint(0, 2, [seq_len, seq_len]).astype(paddle.float32)

            >>> output = paddle.sparse.nn.functional.attention(query, key, value, sp_mask, kp_mask, attn_mask)
            >>> output.backward()
    """
    assert in_dynamic_or_pir_mode(), (
        "Currently, Sparse API only support dynamic mode or pir mode."
    )
    return _C_ops.sparse_fused_attention(
        query, key, value, sparse_mask, key_padding_mask, attn_mask
    )