"""
"""

import torch
from kernels import get_kernel


_flash_attn_func = get_kernel("kernels-community/vllm-flash-attn3").flash_attn_func


@torch.library.custom_op("flash::flash_attn_func", mutates_args=())
def flash_attn_func(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor) -> torch.Tensor:
    outputs, lse = _flash_attn_func(q, k, v)
    return outputs

@flash_attn_func.register_fake
def _(q, k, v, **kwargs):
    # two outputs:
    # 1. output: (batch, seq_len, num_heads, head_dim)
    # 2. softmax_lse: (batch, num_heads, seq_len) with dtype=torch.float32
    meta_q = torch.empty_like(q).contiguous()
    return meta_q #, q.new_empty((q.size(0), q.size(2), q.size(1)), dtype=torch.float32)

# Copied FusedFluxAttnProcessor2_0 but using flash v3 instead of SDPA
class FlashFusedFluxAttnProcessor3_0:
    """Attention processor used typically in processing the SD3-like self-attention projections."""

    def __call__(
        self,
        attn,
        hidden_states: torch.FloatTensor,
        encoder_hidden_states: torch.FloatTensor | None = None,
        attention_mask: torch.FloatTensor | None = None,
        image_rotary_emb: torch.Tensor | None = None,
    ) -> torch.FloatTensor:
        batch_size, _, _ = hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape

        # `sample` projections.
        qkv = attn.to_qkv(hidden_states)
        split_size = qkv.shape[-1] // 3
        query, key, value = torch.split(qkv, split_size, dim=-1)

        inner_dim = key.shape[-1]
        head_dim = inner_dim // attn.heads

        query = query.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        key = key.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)
        value = value.view(batch_size, -1, attn.heads, head_dim).transpose(1, 2)

        if attn.norm_q is not None:
            query = attn.norm_q(query)
        if attn.norm_k is not None:
            key = attn.norm_k(key)

        # the attention in FluxSingleTransformerBlock does not use `encoder_hidden_states`
        # `context` projections.
        if encoder_hidden_states is not None:
            encoder_qkv = attn.to_added_qkv(encoder_hidden_states)
            split_size = encoder_qkv.shape[-1] // 3
            (
                encoder_hidden_states_query_proj,
                encoder_hidden_states_key_proj,
                encoder_hidden_states_value_proj,
            ) = torch.split(encoder_qkv, split_size, dim=-1)

            encoder_hidden_states_query_proj = encoder_hidden_states_query_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)
            encoder_hidden_states_key_proj = encoder_hidden_states_key_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)
            encoder_hidden_states_value_proj = encoder_hidden_states_value_proj.view(
                batch_size, -1, attn.heads, head_dim
            ).transpose(1, 2)

            if attn.norm_added_q is not None:
                encoder_hidden_states_query_proj = attn.norm_added_q(encoder_hidden_states_query_proj)
            if attn.norm_added_k is not None:
                encoder_hidden_states_key_proj = attn.norm_added_k(encoder_hidden_states_key_proj)

            # attention
            query = torch.cat([encoder_hidden_states_query_proj, query], dim=2)
            key = torch.cat([encoder_hidden_states_key_proj, key], dim=2)
            value = torch.cat([encoder_hidden_states_value_proj, value], dim=2)

        if image_rotary_emb is not None:
            from diffusers.models.embeddings import apply_rotary_emb

            query = apply_rotary_emb(query, image_rotary_emb)
            key = apply_rotary_emb(key, image_rotary_emb)

        # NB: transposes are necessary to match expected SDPA input shape
        hidden_states = flash_attn_func(
            query.transpose(1, 2),
            key.transpose(1, 2),
            value.transpose(1, 2))[0].transpose(1, 2)

        hidden_states = hidden_states.transpose(1, 2).reshape(batch_size, -1, attn.heads * head_dim)
        hidden_states = hidden_states.to(query.dtype)

        if encoder_hidden_states is not None:
            encoder_hidden_states, hidden_states = (
                hidden_states[:, : encoder_hidden_states.shape[1]],
                hidden_states[:, encoder_hidden_states.shape[1] :],
            )

            # linear proj
            hidden_states = attn.to_out[0](hidden_states)
            # dropout
            hidden_states = attn.to_out[1](hidden_states)
            encoder_hidden_states = attn.to_add_out(encoder_hidden_states)

            return hidden_states, encoder_hidden_states
        else:
            return hidden_states