# Copyright 2024-2025 The Alibaba Wan Team Authors. All rights reserved.
import torch
import math
import torch.nn.functional as F

try:
    import flash_attn_interface
    FLASH_ATTN_3_AVAILABLE = True
except ModuleNotFoundError:
    FLASH_ATTN_3_AVAILABLE = False

try:
    import flash_attn
    FLASH_ATTN_2_AVAILABLE = True
except ModuleNotFoundError:
    FLASH_ATTN_2_AVAILABLE = False

try:
    # raise NotImplementedError
    from sageattention import sageattn_varlen, sageattn
    print('Sage Attn is installed!')
except:
    print('Sage Attn is not installed!')
    sageattn_varlen = None
    sageattn = None

import warnings

__all__ = [
    'flash_attention',
    'attention',
]


def flash_attention(
    q,
    k,
    v,
    q_lens=None,
    k_lens=None,
    dropout_p=0.,
    softmax_scale=None,
    q_scale=None,
    causal=False,
    window_size=(-1, -1),
    deterministic=False,
    dtype=torch.bfloat16,
    version=None,
):
    """
    q:              [B, Lq, Nq, C1].
    k:              [B, Lk, Nk, C1].
    v:              [B, Lk, Nk, C2]. Nq must be divisible by Nk.
    q_lens:         [B].
    k_lens:         [B].
    dropout_p:      float. Dropout probability.
    softmax_scale:  float. The scaling of QK^T before applying softmax.
    causal:         bool. Whether to apply causal attention mask.
    window_size:    (left right). If not (-1, -1), apply sliding window local attention.
    deterministic:  bool. If True, slightly slower and uses more memory.
    dtype:          torch.dtype. Apply when dtype of q/k/v is not float16/bfloat16.
    """
    half_dtypes = (torch.float16, torch.bfloat16)
    assert dtype in half_dtypes
    assert q.device.type == 'cuda' and q.size(-1) <= 256

    # params
    b, lq, lk, out_dtype = q.size(0), q.size(1), k.size(1), q.dtype

    def half(x):
        return x if x.dtype in half_dtypes else x.to(dtype)

    # preprocess query
    if q_lens is None:
        q = half(q.flatten(0, 1))
        q_lens = torch.tensor(
            [lq] * b, dtype=torch.int32).to(
                device=q.device, non_blocking=True)
    else:
        q = half(torch.cat([u[:v] for u, v in zip(q, q_lens)]))

    # preprocess key, value
    if k_lens is None:
        k = half(k.flatten(0, 1))
        v = half(v.flatten(0, 1))
        k_lens = torch.tensor(
            [lk] * b, dtype=torch.int32).to(
                device=k.device, non_blocking=True)
    else:
        k = half(torch.cat([u[:v] for u, v in zip(k, k_lens)]))
        v = half(torch.cat([u[:v] for u, v in zip(v, k_lens)]))

    q = q.to(v.dtype)
    k = k.to(v.dtype)

    if q_scale is not None:
        q = q * q_scale

    if version is not None and version == 3 and not FLASH_ATTN_3_AVAILABLE:
        warnings.warn(
            'Flash attention 3 is not available, use flash attention 2 instead.'
        )

    # apply attention
    if sageattn_varlen is not None:
        x = sageattn_varlen(
                q=q,
                k=k,
                v=v,
                cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
                    0, dtype=torch.int32).to(q.device, non_blocking=True),
                cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
                    0, dtype=torch.int32).to(q.device, non_blocking=True),
                max_seqlen_q=lq,
                max_seqlen_k=lk,
                dropout_p=dropout_p,
                softmax_scale=softmax_scale,
                causal=causal,
                window_size=window_size,
                deterministic=deterministic).unflatten(0, (b, lq))
        print('using sageattention')
    elif (version is None or version == 3) and FLASH_ATTN_3_AVAILABLE:
        # Note: dropout_p, window_size are not supported in FA3 now.
        x = flash_attn_interface.flash_attn_varlen_func(
            q=q,
            k=k,
            v=v,
            cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
                0, dtype=torch.int32).to(q.device, non_blocking=True),
            cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
                0, dtype=torch.int32).to(q.device, non_blocking=True),
            seqused_q=None,
            seqused_k=None,
            max_seqlen_q=lq,
            max_seqlen_k=lk,
            softmax_scale=softmax_scale,
            causal=causal,
            deterministic=deterministic)[0].unflatten(0, (b, lq))
    else:
        assert FLASH_ATTN_2_AVAILABLE
        x = flash_attn.flash_attn_varlen_func(
            q=q,
            k=k,
            v=v,
            cu_seqlens_q=torch.cat([q_lens.new_zeros([1]), q_lens]).cumsum(
                0, dtype=torch.int32).to(q.device, non_blocking=True),
            cu_seqlens_k=torch.cat([k_lens.new_zeros([1]), k_lens]).cumsum(
                0, dtype=torch.int32).to(q.device, non_blocking=True),
            max_seqlen_q=lq,
            max_seqlen_k=lk,
            dropout_p=dropout_p,
            softmax_scale=softmax_scale,
            causal=causal,
            window_size=window_size,
            deterministic=deterministic).unflatten(0, (b, lq))
        # print('flash_attn')

    # output
    return x.type(out_dtype)


def attention(
    q,
    k,
    v,
    q_lens=None,
    k_lens=None,
    dropout_p=0.,
    softmax_scale=None,
    q_scale=None,
    causal=False,
    window_size=(-1, -1),
    deterministic=False,
    dtype=torch.bfloat16,
    fa_version=None,
):
    # if FLASH_ATTN_2_AVAILABLE or FLASH_ATTN_3_AVAILABLE:
    #     return flash_attention(
    #         q=q,
    #         k=k,
    #         v=v,
    #         q_lens=q_lens,
    #         k_lens=k_lens,
    #         dropout_p=dropout_p,
    #         softmax_scale=softmax_scale,
    #         q_scale=q_scale,
    #         causal=causal,
    #         window_size=window_size,
    #         deterministic=deterministic,
    #         dtype=dtype,
    #         version=fa_version,
    #     )
    # else:
    if q_lens is not None or k_lens is not None:
        warnings.warn(
            'Padding mask is disabled when using scaled_dot_product_attention. It can have a significant impact on performance.'
        )
    attn_mask = None

    q = q.transpose(1, 2).to(dtype)
    k = k.transpose(1, 2).to(dtype)
    v = v.transpose(1, 2).to(dtype)

    # out = torch.nn.functional.scaled_dot_product_attention(
    #     q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p)
    out = manual_scaled_dot_product_attention(
        q, k, v, attn_mask=attn_mask, is_causal=causal, dropout_p=dropout_p)

    out = out.transpose(1, 2).contiguous()
    return out


def manual_scaled_dot_product_attention(query, key, value, attn_mask=None, dropout_p=0.0, is_causal=False):
    # query, key, value shapes: (..., seq_len, head_dim)
    # attn_mask (optional): (..., seq_len, seq_len)

    scale = 1.0 / math.sqrt(query.size(-1))
    scores = torch.matmul(query, key.transpose(-2, -1)) * scale  # (..., seq_len, seq_len)

    # Apply causal mask (if needed)
    if is_causal:
        mask = torch.triu(torch.ones_like(scores, dtype=torch.bool), diagonal=1)
        scores = scores.masked_fill(mask, float('-inf'))

    # Apply additional attention mask (if provided)
    if attn_mask is not None:
        scores = scores + attn_mask

    attn_weights = F.softmax(scores, dim=-1)  # (..., seq_len, seq_len)
    attn_weights = F.dropout(attn_weights, p=dropout_p)  # Optional dropout

    return torch.matmul(attn_weights, value)  # (..., seq_len, head_dim)