from functools import partial

import torch
import torch.nn.functional as F
from einops import rearrange, repeat
from torch import einsum, nn

try:
    import xformers
    import xformers.ops

    XFORMERS_IS_AVAILBLE = True
except:
    XFORMERS_IS_AVAILBLE = False
from lvdm.basics import zero_module
from lvdm.common import checkpoint, default, exists


class RelativePosition(nn.Module):
    """https://github.com/evelinehong/Transformer_Relative_Position_PyTorch/blob/master/relative_position.py"""

    def __init__(self, num_units, max_relative_position):
        super().__init__()
        self.num_units = num_units
        self.max_relative_position = max_relative_position
        self.embeddings_table = nn.Parameter(torch.Tensor(max_relative_position * 2 + 1, num_units))
        nn.init.xavier_uniform_(self.embeddings_table)

    def forward(self, length_q, length_k):
        device = self.embeddings_table.device
        range_vec_q = torch.arange(length_q, device=device)
        range_vec_k = torch.arange(length_k, device=device)
        distance_mat = range_vec_k[None, :] - range_vec_q[:, None]
        distance_mat_clipped = torch.clamp(distance_mat, -self.max_relative_position, self.max_relative_position)
        final_mat = distance_mat_clipped + self.max_relative_position
        final_mat = final_mat.long()
        embeddings = self.embeddings_table[final_mat]
        return embeddings


class CrossAttention(nn.Module):
    def __init__(
        self,
        query_dim,
        context_dim=None,
        heads=8,
        dim_head=64,
        dropout=0.0,
        relative_position=False,
        temporal_length=None,
        video_length=None,
        image_cross_attention=False,
        image_cross_attention_scale=1.0,
        image_cross_attention_scale_learnable=False,
        text_context_len=77,
    ):
        super().__init__()
        inner_dim = dim_head * heads
        context_dim = default(context_dim, query_dim)

        self.scale = dim_head**-0.5
        self.heads = heads
        self.dim_head = dim_head
        self.to_q = nn.Linear(query_dim, inner_dim, bias=False)
        self.to_k = nn.Linear(context_dim, inner_dim, bias=False)
        self.to_v = nn.Linear(context_dim, inner_dim, bias=False)

        self.to_out = nn.Sequential(nn.Linear(inner_dim, query_dim), nn.Dropout(dropout))

        self.relative_position = relative_position
        if self.relative_position:
            assert temporal_length is not None
            self.relative_position_k = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
            self.relative_position_v = RelativePosition(num_units=dim_head, max_relative_position=temporal_length)
        else:
            ## only used for spatial attention, while NOT for temporal attention
            if XFORMERS_IS_AVAILBLE and temporal_length is None:
                self.forward = self.efficient_forward

        self.video_length = video_length
        self.image_cross_attention = image_cross_attention
        self.image_cross_attention_scale = image_cross_attention_scale
        self.text_context_len = text_context_len
        self.image_cross_attention_scale_learnable = image_cross_attention_scale_learnable
        if self.image_cross_attention:
            self.to_k_ip = nn.Linear(context_dim, inner_dim, bias=False)
            self.to_v_ip = nn.Linear(context_dim, inner_dim, bias=False)
            if image_cross_attention_scale_learnable:
                self.register_parameter("alpha", nn.Parameter(torch.tensor(0.0)))

    def forward(self, x, context=None, mask=None):
        spatial_self_attn = context is None
        k_ip, v_ip, out_ip = None, None, None

        h = self.heads
        q = self.to_q(x)
        context = default(context, x)

        if self.image_cross_attention and not spatial_self_attn:
            context, context_image = context[:, : self.text_context_len, :], context[:, self.text_context_len :, :]
            k = self.to_k(context)
            v = self.to_v(context)
            k_ip = self.to_k_ip(context_image)
            v_ip = self.to_v_ip(context_image)
        else:
            if not spatial_self_attn:
                context = context[:, : self.text_context_len, :]
            k = self.to_k(context)
            v = self.to_v(context)

        q, k, v = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (q, k, v))

        sim = torch.einsum("b i d, b j d -> b i j", q, k) * self.scale
        if self.relative_position:
            len_q, len_k, len_v = q.shape[1], k.shape[1], v.shape[1]
            k2 = self.relative_position_k(len_q, len_k)
            sim2 = einsum("b t d, t s d -> b t s", q, k2) * self.scale  # TODO check
            sim += sim2
        del k

        if exists(mask):
            ## feasible for causal attention mask only
            max_neg_value = -torch.finfo(sim.dtype).max
            mask = repeat(mask, "b i j -> (b h) i j", h=h)
            sim.masked_fill_(~(mask > 0.5), max_neg_value)

        # attention, what we cannot get enough of
        sim = sim.softmax(dim=-1)

        out = torch.einsum("b i j, b j d -> b i d", sim, v)
        if self.relative_position:
            v2 = self.relative_position_v(len_q, len_v)
            out2 = einsum("b t s, t s d -> b t d", sim, v2)  # TODO check
            out += out2
        out = rearrange(out, "(b h) n d -> b n (h d)", h=h)

        ## for image cross-attention
        if k_ip is not None:
            k_ip, v_ip = map(lambda t: rearrange(t, "b n (h d) -> (b h) n d", h=h), (k_ip, v_ip))
            sim_ip = torch.einsum("b i d, b j d -> b i j", q, k_ip) * self.scale
            del k_ip
            sim_ip = sim_ip.softmax(dim=-1)
            out_ip = torch.einsum("b i j, b j d -> b i d", sim_ip, v_ip)
            out_ip = rearrange(out_ip, "(b h) n d -> b n (h d)", h=h)

        if out_ip is not None:
            if self.image_cross_attention_scale_learnable:
                out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha) + 1)
            else:
                out = out + self.image_cross_attention_scale * out_ip

        return self.to_out(out)

    def efficient_forward(self, x, context=None, mask=None):
        spatial_self_attn = context is None
        k_ip, v_ip, out_ip = None, None, None

        q = self.to_q(x)
        context = default(context, x)

        if self.image_cross_attention and not spatial_self_attn:
            context, context_image = context[:, : self.text_context_len, :], context[:, self.text_context_len :, :]
            k = self.to_k(context)
            v = self.to_v(context)
            k_ip = self.to_k_ip(context_image)
            v_ip = self.to_v_ip(context_image)
        else:
            if not spatial_self_attn:
                context = context[:, : self.text_context_len, :]
            k = self.to_k(context)
            v = self.to_v(context)

        b, _, _ = q.shape
        q, k, v = map(
            lambda t: t.unsqueeze(3)
            .reshape(b, t.shape[1], self.heads, self.dim_head)
            .permute(0, 2, 1, 3)
            .reshape(b * self.heads, t.shape[1], self.dim_head)
            .contiguous(),
            (q, k, v),
        )
        # actually compute the attention, what we cannot get enough of
        out = xformers.ops.memory_efficient_attention(q, k, v, attn_bias=None, op=None)

        ## for image cross-attention
        if k_ip is not None:
            k_ip, v_ip = map(
                lambda t: t.unsqueeze(3)
                .reshape(b, t.shape[1], self.heads, self.dim_head)
                .permute(0, 2, 1, 3)
                .reshape(b * self.heads, t.shape[1], self.dim_head)
                .contiguous(),
                (k_ip, v_ip),
            )
            out_ip = xformers.ops.memory_efficient_attention(q, k_ip, v_ip, attn_bias=None, op=None)
            out_ip = (
                out_ip.unsqueeze(0)
                .reshape(b, self.heads, out.shape[1], self.dim_head)
                .permute(0, 2, 1, 3)
                .reshape(b, out.shape[1], self.heads * self.dim_head)
            )

        if exists(mask):
            raise NotImplementedError
        out = (
            out.unsqueeze(0)
            .reshape(b, self.heads, out.shape[1], self.dim_head)
            .permute(0, 2, 1, 3)
            .reshape(b, out.shape[1], self.heads * self.dim_head)
        )
        if out_ip is not None:
            if self.image_cross_attention_scale_learnable:
                out = out + self.image_cross_attention_scale * out_ip * (torch.tanh(self.alpha) + 1)
            else:
                out = out + self.image_cross_attention_scale * out_ip

        return self.to_out(out)


class BasicTransformerBlock(nn.Module):
    def __init__(
        self,
        dim,
        n_heads,
        d_head,
        dropout=0.0,
        context_dim=None,
        gated_ff=True,
        checkpoint=True,
        disable_self_attn=False,
        attention_cls=None,
        video_length=None,
        image_cross_attention=False,
        image_cross_attention_scale=1.0,
        image_cross_attention_scale_learnable=False,
        text_context_len=77,
    ):
        super().__init__()
        attn_cls = CrossAttention if attention_cls is None else attention_cls
        self.disable_self_attn = disable_self_attn
        self.attn1 = attn_cls(
            query_dim=dim,
            heads=n_heads,
            dim_head=d_head,
            dropout=dropout,
            context_dim=context_dim if self.disable_self_attn else None,
        )
        self.ff = FeedForward(dim, dropout=dropout, glu=gated_ff)
        self.attn2 = attn_cls(
            query_dim=dim,
            context_dim=context_dim,
            heads=n_heads,
            dim_head=d_head,
            dropout=dropout,
            video_length=video_length,
            image_cross_attention=image_cross_attention,
            image_cross_attention_scale=image_cross_attention_scale,
            image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,
            text_context_len=text_context_len,
        )
        self.image_cross_attention = image_cross_attention

        self.norm1 = nn.LayerNorm(dim)
        self.norm2 = nn.LayerNorm(dim)
        self.norm3 = nn.LayerNorm(dim)
        self.checkpoint = checkpoint

    def forward(self, x, context=None, mask=None, **kwargs):
        ## implementation tricks: because checkpointing doesn't support non-tensor (e.g. None or scalar) arguments
        input_tuple = (x,)  ## should not be (x), otherwise *input_tuple will decouple x into multiple arguments
        if context is not None:
            input_tuple = (x, context)
        if mask is not None:
            forward_mask = partial(self._forward, mask=mask)
            return checkpoint(forward_mask, (x,), self.parameters(), self.checkpoint)
        return checkpoint(self._forward, input_tuple, self.parameters(), self.checkpoint)

    def _forward(self, x, context=None, mask=None):
        x = self.attn1(self.norm1(x), context=context if self.disable_self_attn else None, mask=mask) + x
        x = self.attn2(self.norm2(x), context=context, mask=mask) + x
        x = self.ff(self.norm3(x)) + x
        return x


class SpatialTransformer(nn.Module):
    """
    Transformer block for image-like data in spatial axis.
    First, project the input (aka embedding)
    and reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    NEW: use_linear for more efficiency instead of the 1x1 convs
    """

    def __init__(
        self,
        in_channels,
        n_heads,
        d_head,
        depth=1,
        dropout=0.0,
        context_dim=None,
        use_checkpoint=True,
        disable_self_attn=False,
        use_linear=False,
        video_length=None,
        image_cross_attention=False,
        image_cross_attention_scale_learnable=False,
    ):
        super().__init__()
        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        if not use_linear:
            self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
        else:
            self.proj_in = nn.Linear(in_channels, inner_dim)

        attention_cls = None
        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim,
                    n_heads,
                    d_head,
                    dropout=dropout,
                    context_dim=context_dim,
                    disable_self_attn=disable_self_attn,
                    checkpoint=use_checkpoint,
                    attention_cls=attention_cls,
                    video_length=video_length,
                    image_cross_attention=image_cross_attention,
                    image_cross_attention_scale_learnable=image_cross_attention_scale_learnable,
                )
                for d in range(depth)
            ]
        )
        if not use_linear:
            self.proj_out = zero_module(nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
        else:
            self.proj_out = zero_module(nn.Linear(inner_dim, in_channels))
        self.use_linear = use_linear

    def forward(self, x, context=None, **kwargs):
        b, c, h, w = x.shape
        x_in = x
        x = self.norm(x)
        if not self.use_linear:
            x = self.proj_in(x)
        x = rearrange(x, "b c h w -> b (h w) c").contiguous()
        if self.use_linear:
            x = self.proj_in(x)
        for i, block in enumerate(self.transformer_blocks):
            x = block(x, context=context, **kwargs)
        if self.use_linear:
            x = self.proj_out(x)
        x = rearrange(x, "b (h w) c -> b c h w", h=h, w=w).contiguous()
        if not self.use_linear:
            x = self.proj_out(x)
        return x + x_in


class TemporalTransformer(nn.Module):
    """
    Transformer block for image-like data in temporal axis.
    First, reshape to b, t, d.
    Then apply standard transformer action.
    Finally, reshape to image
    """

    def __init__(
        self,
        in_channels,
        n_heads,
        d_head,
        depth=1,
        dropout=0.0,
        context_dim=None,
        use_checkpoint=True,
        use_linear=False,
        only_self_att=True,
        causal_attention=False,
        causal_block_size=1,
        relative_position=False,
        temporal_length=None,
    ):
        super().__init__()
        self.only_self_att = only_self_att
        self.relative_position = relative_position
        self.causal_attention = causal_attention
        self.causal_block_size = causal_block_size

        self.in_channels = in_channels
        inner_dim = n_heads * d_head
        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
        if not use_linear:
            self.proj_in = nn.Conv1d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0)
        else:
            self.proj_in = nn.Linear(in_channels, inner_dim)

        if relative_position:
            assert temporal_length is not None
            attention_cls = partial(CrossAttention, relative_position=True, temporal_length=temporal_length)
        else:
            attention_cls = partial(CrossAttention, temporal_length=temporal_length)
        if self.causal_attention:
            assert temporal_length is not None
            self.mask = torch.tril(torch.ones([1, temporal_length, temporal_length]))

        if self.only_self_att:
            context_dim = None
        self.transformer_blocks = nn.ModuleList(
            [
                BasicTransformerBlock(
                    inner_dim,
                    n_heads,
                    d_head,
                    dropout=dropout,
                    context_dim=context_dim,
                    attention_cls=attention_cls,
                    checkpoint=use_checkpoint,
                )
                for d in range(depth)
            ]
        )
        if not use_linear:
            self.proj_out = zero_module(nn.Conv1d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0))
        else:
            self.proj_out = zero_module(nn.Linear(inner_dim, in_channels))
        self.use_linear = use_linear

    def forward(self, x, context=None):
        b, c, t, h, w = x.shape
        x_in = x
        x = self.norm(x)
        x = rearrange(x, "b c t h w -> (b h w) c t").contiguous()
        if not self.use_linear:
            x = self.proj_in(x)
        x = rearrange(x, "bhw c t -> bhw t c").contiguous()
        if self.use_linear:
            x = self.proj_in(x)

        temp_mask = None
        if self.causal_attention:
            # slice the from mask map
            temp_mask = self.mask[:, :t, :t].to(x.device)

        if temp_mask is not None:
            mask = temp_mask.to(x.device)
            mask = repeat(mask, "l i j -> (l bhw) i j", bhw=b * h * w)
        else:
            mask = None

        if self.only_self_att:
            ## note: if no context is given, cross-attention defaults to self-attention
            for i, block in enumerate(self.transformer_blocks):
                x = block(x, mask=mask)
            x = rearrange(x, "(b hw) t c -> b hw t c", b=b).contiguous()
        else:
            x = rearrange(x, "(b hw) t c -> b hw t c", b=b).contiguous()
            context = rearrange(context, "(b t) l con -> b t l con", t=t).contiguous()
            for i, block in enumerate(self.transformer_blocks):
                # calculate each batch one by one (since number in shape could not greater then 65,535 for some package)
                for j in range(b):
                    context_j = repeat(context[j], "t l con -> (t r) l con", r=(h * w) // t, t=t).contiguous()
                    ## note: causal mask will not applied in cross-attention case
                    x[j] = block(x[j], context=context_j)

        if self.use_linear:
            x = self.proj_out(x)
            x = rearrange(x, "b (h w) t c -> b c t h w", h=h, w=w).contiguous()
        if not self.use_linear:
            x = rearrange(x, "b hw t c -> (b hw) c t").contiguous()
            x = self.proj_out(x)
            x = rearrange(x, "(b h w) c t -> b c t h w", b=b, h=h, w=w).contiguous()

        return x + x_in


class GEGLU(nn.Module):
    def __init__(self, dim_in, dim_out):
        super().__init__()
        self.proj = nn.Linear(dim_in, dim_out * 2)

    def forward(self, x):
        x, gate = self.proj(x).chunk(2, dim=-1)
        return x * F.gelu(gate)


class FeedForward(nn.Module):
    def __init__(self, dim, dim_out=None, mult=4, glu=False, dropout=0.0):
        super().__init__()
        inner_dim = int(dim * mult)
        dim_out = default(dim_out, dim)
        project_in = nn.Sequential(nn.Linear(dim, inner_dim), nn.GELU()) if not glu else GEGLU(dim, inner_dim)

        self.net = nn.Sequential(project_in, nn.Dropout(dropout), nn.Linear(inner_dim, dim_out))

    def forward(self, x):
        return self.net(x)


class LinearAttention(nn.Module):
    def __init__(self, dim, heads=4, dim_head=32):
        super().__init__()
        self.heads = heads
        hidden_dim = dim_head * heads
        self.to_qkv = nn.Conv2d(dim, hidden_dim * 3, 1, bias=False)
        self.to_out = nn.Conv2d(hidden_dim, dim, 1)

    def forward(self, x):
        b, c, h, w = x.shape
        qkv = self.to_qkv(x)
        q, k, v = rearrange(qkv, "b (qkv heads c) h w -> qkv b heads c (h w)", heads=self.heads, qkv=3)
        k = k.softmax(dim=-1)
        context = torch.einsum("bhdn,bhen->bhde", k, v)
        out = torch.einsum("bhde,bhdn->bhen", context, q)
        out = rearrange(out, "b heads c (h w) -> b (heads c) h w", heads=self.heads, h=h, w=w)
        return self.to_out(out)


class SpatialSelfAttention(nn.Module):
    def __init__(self, in_channels):
        super().__init__()
        self.in_channels = in_channels

        self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
        self.q = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.k = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.v = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)
        self.proj_out = torch.nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0)

    def forward(self, x):
        h_ = x
        h_ = self.norm(h_)
        q = self.q(h_)
        k = self.k(h_)
        v = self.v(h_)

        # compute attention
        b, c, h, w = q.shape
        q = rearrange(q, "b c h w -> b (h w) c")
        k = rearrange(k, "b c h w -> b c (h w)")
        w_ = torch.einsum("bij,bjk->bik", q, k)

        w_ = w_ * (int(c) ** (-0.5))
        w_ = torch.nn.functional.softmax(w_, dim=2)

        # attend to values
        v = rearrange(v, "b c h w -> b c (h w)")
        w_ = rearrange(w_, "b i j -> b j i")
        h_ = torch.einsum("bij,bjk->bik", v, w_)
        h_ = rearrange(h_, "b c (h w) -> b c h w", h=h)
        h_ = self.proj_out(h_)

        return x + h_
