import torch

from ..modules.attention import *
from ..modules.diffusionmodules.util import (AlphaBlender, linear,
                                             timestep_embedding)


class TimeMixSequential(nn.Sequential):

    def forward(self, x, context=None, timesteps=None):
        for layer in self:
            x = layer(x, context, timesteps)

        return x


class VideoTransformerBlock(nn.Module):
    ATTENTION_MODES = {
        'softmax': CrossAttention,
        'softmax-xformers': MemoryEfficientCrossAttention,
    }

    def __init__(
        self,
        dim,
        n_heads,
        d_head,
        dropout=0.0,
        context_dim=None,
        gated_ff=True,
        checkpoint=True,
        timesteps=None,
        ff_in=False,
        inner_dim=None,
        attn_mode='softmax',
        disable_self_attn=False,
        disable_temporal_crossattention=False,
        switch_temporal_ca_to_sa=False,
    ):
        super().__init__()

        attn_cls = self.ATTENTION_MODES[attn_mode]

        self.ff_in = ff_in or inner_dim is not None
        if inner_dim is None:
            inner_dim = dim

        assert int(n_heads * d_head) == inner_dim

        self.is_res = inner_dim == dim

        if self.ff_in:
            self.norm_in = nn.LayerNorm(dim)
            self.ff_in = FeedForward(dim,
                                     dim_out=inner_dim,
                                     dropout=dropout,
                                     glu=gated_ff)

        self.timesteps = timesteps
        self.disable_self_attn = disable_self_attn
        if self.disable_self_attn:
            self.attn1 = attn_cls(
                query_dim=inner_dim,
                heads=n_heads,
                dim_head=d_head,
                context_dim=context_dim,
                dropout=dropout,
            )  # is a cross-attention
        else:
            self.attn1 = attn_cls(query_dim=inner_dim,
                                  heads=n_heads,
                                  dim_head=d_head,
                                  dropout=dropout)  # is a self-attention

        self.ff = FeedForward(inner_dim,
                              dim_out=dim,
                              dropout=dropout,
                              glu=gated_ff)

        if disable_temporal_crossattention:
            if switch_temporal_ca_to_sa:
                raise ValueError
            else:
                self.attn2 = None
        else:
            self.norm2 = nn.LayerNorm(inner_dim)
            if switch_temporal_ca_to_sa:
                self.attn2 = attn_cls(query_dim=inner_dim,
                                      heads=n_heads,
                                      dim_head=d_head,
                                      dropout=dropout)  # is a self-attention
            else:
                self.attn2 = attn_cls(
                    query_dim=inner_dim,
                    context_dim=context_dim,
                    heads=n_heads,
                    dim_head=d_head,
                    dropout=dropout,
                )  # is self-attn if context is none

        self.norm1 = nn.LayerNorm(inner_dim)
        self.norm3 = nn.LayerNorm(inner_dim)
        self.switch_temporal_ca_to_sa = switch_temporal_ca_to_sa

        self.checkpoint = checkpoint
        if self.checkpoint:
            print(f'{self.__class__.__name__} is using checkpointing')

    def forward(self,
                x: torch.Tensor,
                context: torch.Tensor = None,
                timesteps: int = None) -> torch.Tensor:
        if self.checkpoint:
            return checkpoint(self._forward, x, context, timesteps)
        else:
            return self._forward(x, context, timesteps=timesteps)

    def _forward(self, x, context=None, timesteps=None):
        assert self.timesteps or timesteps
        assert not (self.timesteps
                    and timesteps) or self.timesteps == timesteps
        timesteps = self.timesteps or timesteps
        B, S, C = x.shape
        x = rearrange(x, '(b t) s c -> (b s) t c', t=timesteps)

        if self.ff_in:
            x_skip = x
            x = self.ff_in(self.norm_in(x))
            if self.is_res:
                x += x_skip

        if self.disable_self_attn:
            x = self.attn1(self.norm1(x), context=context) + x
        else:
            x = self.attn1(self.norm1(x)) + x

        if self.attn2 is not None:
            if self.switch_temporal_ca_to_sa:
                x = self.attn2(self.norm2(x)) + x
            else:
                x = self.attn2(self.norm2(x), context=context) + x
        x_skip = x
        x = self.ff(self.norm3(x))
        if self.is_res:
            x += x_skip

        x = rearrange(x,
                      '(b s) t c -> (b t) s c',
                      s=S,
                      b=B // timesteps,
                      c=C,
                      t=timesteps)
        return x

    def get_last_layer(self):
        return self.ff.net[-1].weight


str_to_dtype = {
    'fp32': torch.float32,
    'fp16': torch.float16,
    'bf16': torch.bfloat16
}


class SpatialVideoTransformer(SpatialTransformer):

    def __init__(
        self,
        in_channels,
        n_heads,
        d_head,
        depth=1,
        dropout=0.0,
        use_linear=False,
        context_dim=None,
        use_spatial_context=False,
        timesteps=None,
        merge_strategy: str = 'fixed',
        merge_factor: float = 0.5,
        time_context_dim=None,
        ff_in=False,
        checkpoint=False,
        time_depth=1,
        attn_mode='softmax',
        disable_self_attn=False,
        disable_temporal_crossattention=False,
        max_time_embed_period: int = 10000,
        dtype='fp32',
    ):
        super().__init__(
            in_channels,
            n_heads,
            d_head,
            depth=depth,
            dropout=dropout,
            attn_type=attn_mode,
            use_checkpoint=checkpoint,
            context_dim=context_dim,
            use_linear=use_linear,
            disable_self_attn=disable_self_attn,
        )
        self.time_depth = time_depth
        self.depth = depth
        self.max_time_embed_period = max_time_embed_period

        time_mix_d_head = d_head
        n_time_mix_heads = n_heads

        time_mix_inner_dim = int(time_mix_d_head * n_time_mix_heads)

        inner_dim = n_heads * d_head
        if use_spatial_context:
            time_context_dim = context_dim

        self.time_stack = nn.ModuleList([
            VideoTransformerBlock(
                inner_dim,
                n_time_mix_heads,
                time_mix_d_head,
                dropout=dropout,
                context_dim=time_context_dim,
                timesteps=timesteps,
                checkpoint=checkpoint,
                ff_in=ff_in,
                inner_dim=time_mix_inner_dim,
                attn_mode=attn_mode,
                disable_self_attn=disable_self_attn,
                disable_temporal_crossattention=disable_temporal_crossattention,
            ) for _ in range(self.depth)
        ])

        assert len(self.time_stack) == len(self.transformer_blocks)

        self.use_spatial_context = use_spatial_context
        self.in_channels = in_channels

        time_embed_dim = self.in_channels * 4
        self.time_pos_embed = nn.Sequential(
            linear(self.in_channels, time_embed_dim),
            nn.SiLU(),
            linear(time_embed_dim, self.in_channels),
        )

        self.time_mixer = AlphaBlender(alpha=merge_factor,
                                       merge_strategy=merge_strategy)
        self.dtype = str_to_dtype[dtype]

    def forward(
        self,
        x: torch.Tensor,
        context: Optional[torch.Tensor] = None,
        time_context: Optional[torch.Tensor] = None,
        timesteps: Optional[int] = None,
        image_only_indicator: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        _, _, h, w = x.shape
        x_in = x
        spatial_context = None
        if exists(context):
            spatial_context = context

        if self.use_spatial_context:
            assert context.ndim == 3, f'n dims of spatial context should be 3 but are {context.ndim}'

            time_context = context
            time_context_first_timestep = time_context[::timesteps]
            time_context = repeat(time_context_first_timestep,
                                  'b ... -> (b n) ...',
                                  n=h * w)
        elif time_context is not None and not self.use_spatial_context:
            time_context = repeat(time_context, 'b ... -> (b n) ...', n=h * w)
            if time_context.ndim == 2:
                time_context = rearrange(time_context, 'b c -> b 1 c')

        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')
        if self.use_linear:
            x = self.proj_in(x)

        num_frames = torch.arange(timesteps, device=x.device)
        num_frames = repeat(num_frames, 't -> b t', b=x.shape[0] // timesteps)
        num_frames = rearrange(num_frames, 'b t -> (b t)')
        t_emb = timestep_embedding(
            num_frames,
            self.in_channels,
            repeat_only=False,
            max_period=self.max_time_embed_period,
            dtype=self.dtype,
        )
        emb = self.time_pos_embed(t_emb)
        emb = emb[:, None, :]

        for it_, (block, mix_block) in enumerate(
                zip(self.transformer_blocks, self.time_stack)):
            x = block(
                x,
                context=spatial_context,
            )

            x_mix = x
            x_mix = x_mix + emb

            x_mix = mix_block(x_mix, context=time_context, timesteps=timesteps)
            x = self.time_mixer(
                x_spatial=x,
                x_temporal=x_mix,
                image_only_indicator=image_only_indicator,
            )
        if self.use_linear:
            x = self.proj_out(x)
        x = rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
        if not self.use_linear:
            x = self.proj_out(x)
        out = x + x_in
        return out
