import torch

from backend.args import args

if args.xformers:
    import xformers
    import xformers.ops


def attention_xformers(q, k, v, heads, mask=None):
    b, _, dim_head = q.shape
    dim_head //= heads

    q, k, v = map(
        lambda t: t.unsqueeze(3)
        .reshape(b, -1, heads, dim_head)
        .permute(0, 2, 1, 3)
        .reshape(b * heads, -1, dim_head)
        .contiguous(),
        (q, k, v),
    )

    if mask is not None:
        pad = 8 - q.shape[1] % 8
        mask_out = torch.empty([q.shape[0], q.shape[1], q.shape[1] + pad], dtype=q.dtype, device=q.device)
        mask_out[:, :, :mask.shape[-1]] = mask
        mask = mask_out[:, :, :mask.shape[-1]]

    out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=mask)

    out = (
        out.unsqueeze(0)
        .reshape(b, heads, -1, dim_head)
        .permute(0, 2, 1, 3)
        .reshape(b, -1, heads * dim_head)
    )
    return out


def attention_pytorch(q, k, v, heads, mask=None):
    b, _, dim_head = q.shape
    dim_head //= heads

    q, k, v = map(
        lambda t: t.view(b, -1, heads, dim_head).transpose(1, 2),
        (q, k, v),
    )

    out = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0, is_causal=False)

    out = (
        out.transpose(1, 2).reshape(b, -1, heads * dim_head)
    )
    return out


attention_function = attention_pytorch

if args.xformers:
    print("Using xformers cross attention")
    attention_function = attention_xformers
else:
    print("Using pytorch cross attention")
    attention_function = attention_pytorch


class AttentionProcessorForge:
    def __call__(self, attn, hidden_states, encoder_hidden_states, attention_mask=None, temb=None, *args, **kwargs):
        residual = hidden_states

        if attn.spatial_norm is not None:
            hidden_states = attn.spatial_norm(hidden_states, temb)

        input_ndim = hidden_states.ndim

        if input_ndim == 4:
            batch_size, channel, height, width = hidden_states.shape
            hidden_states = hidden_states.view(batch_size, channel, height * width).transpose(1, 2)

        batch_size, sequence_length, _ = (
            hidden_states.shape if encoder_hidden_states is None else encoder_hidden_states.shape
        )

        if attention_mask is not None:
            attention_mask = attn.prepare_attention_mask(attention_mask, sequence_length, batch_size)
            attention_mask = attention_mask.view(batch_size, attn.heads, -1, attention_mask.shape[-1])

        if attn.group_norm is not None:
            hidden_states = attn.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2)

        query = attn.to_q(hidden_states)

        if encoder_hidden_states is None:
            encoder_hidden_states = hidden_states
        elif attn.norm_cross:
            encoder_hidden_states = attn.norm_encoder_hidden_states(encoder_hidden_states)

        key = attn.to_k(encoder_hidden_states)
        value = attn.to_v(encoder_hidden_states)

        hidden_states = attention_function(query, key, value, heads=attn.heads, mask=attention_mask)

        hidden_states = attn.to_out[0](hidden_states)
        hidden_states = attn.to_out[1](hidden_states)

        if input_ndim == 4:
            hidden_states = hidden_states.transpose(-1, -2).reshape(batch_size, channel, height, width)

        if attn.residual_connection:
            hidden_states = hidden_states + residual

        hidden_states = hidden_states / attn.rescale_output_factor

        return hidden_states