import torch
import xformers.ops
import torch.nn.functional as F

from torch import nn
from einops import rearrange, repeat
from functools import partial
from diffusers_vdm.basics import zero_module, checkpoint, default, make_temporal_window


def sdp(q, k, v, heads):
    b, _, C = q.shape
    dim_head = C // heads

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

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

    out = (
        out.unsqueeze(0)
        .reshape(b, heads, out.shape[1], dim_head)
        .permute(0, 2, 1, 3)
        .reshape(b, out.shape[1], heads * dim_head)
    )

    return out


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., 
                 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, temporal_window_for_spatial_self_attention=False):
        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.is_temporal_attention = temporal_length is not None

        self.relative_position = relative_position
        if self.relative_position:
            assert self.is_temporal_attention
            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)

        self.video_length = video_length
        self.temporal_window_for_spatial_self_attention = temporal_window_for_spatial_self_attention
        self.temporal_window_type = 'prv'

        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.)) )

    def forward(self, x, context=None, mask=None):
        if self.is_temporal_attention:
            return self.temporal_forward(x, context=context, mask=mask)
        else:
            return self.spatial_forward(x, context=context, mask=mask)

    def temporal_forward(self, x, context=None, mask=None):
        assert mask is None, 'Attention mask not implemented!'
        assert context is None, 'Temporal attention only supports self attention!'

        q = self.to_q(x)
        k = self.to_k(x)
        v = self.to_v(x)

        out = sdp(q, k, v, self.heads)

        return self.to_out(out)
    
    def spatial_forward(self, x, context=None, mask=None):
        assert mask is None, 'Attention mask not implemented!'

        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 spatial_self_attn:
            k = self.to_k(context)
            v = self.to_v(context)

            if self.temporal_window_for_spatial_self_attention:
                k = make_temporal_window(k, t=self.video_length, method=self.temporal_window_type)
                v = make_temporal_window(v, t=self.video_length, method=self.temporal_window_type)
        elif self.image_cross_attention:
            context, context_image = context
            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:
            raise NotImplementedError('Traditional prompt-only attention without IP-Adapter is illegal now.')

        out = sdp(q, k, v, self.heads)

        if k_ip is not None:
            out_ip = sdp(q, k_ip, v_ip, self.heads)

            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., 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, video_length=video_length)
        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., 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., 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.):
        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)