from einops import rearrange
from flash_attn import flash_attn_varlen_qkvpacked_func
from flash_attn.bert_padding import pad_input, unpad_input
import torch


# @torch._dynamo.disable
def flash_attn_no_pad(qkv, key_padding_mask, causal=False, dropout_p=0.0, softmax_scale=None):
    # adapted from https://github.com/Dao-AILab/flash-attention/blob/13403e81157ba37ca525890f2f0f2137edf75311/flash_attn/flash_attention.py#L27
    batch_size = qkv.shape[0]
    seqlen = qkv.shape[1]
    nheads = qkv.shape[-2]
    x = rearrange(qkv, "b s three h d -> b s (three h d)")
    x_unpad, indices, cu_seqlens, max_s = unpad_input(x, key_padding_mask)

    x_unpad = rearrange(x_unpad, "nnz (three h d) -> nnz three h d", three=3, h=nheads)
    output_unpad = flash_attn_varlen_qkvpacked_func(
        x_unpad,
        cu_seqlens,
        max_s,
        dropout_p,
        softmax_scale=softmax_scale,
        causal=causal,
    )
    output = rearrange(
        pad_input(rearrange(output_unpad, "nnz h d -> nnz (h d)"), indices, batch_size, seqlen),
        "b s (h d) -> b s h d",
        h=nheads,
    )
    return output
