from typing import Tuple
import torch
import torch.nn as nn
import math
from einops import rearrange
from torch.nn.functional import scaled_dot_product_attention

def modulate(x, shift, scale):
    return x * (1 + scale) + shift

class Embed(nn.Module):
    def __init__(
            self,
            in_chans: int = 3,
            embed_dim: int = 768,
            norm_layer = None,
            bias: bool = True,
    ):
        super().__init__()
        self.in_chans = in_chans
        self.embed_dim = embed_dim
        self.proj = nn.Linear(in_chans, embed_dim, bias=bias)
        self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
    def forward(self, x):
        x = self.proj(x)
        x = self.norm(x)
        return x

class PatchEmbed(nn.Module):
    def __init__(
        self,
        in_channels=8,
        embed_dim=1152,
        bias=True,
        patch_size=1,
    ):
        super().__init__()

        self.patch_h, self.patch_w = patch_size

        self.patch_size = patch_size
        self.proj = nn.Linear(in_channels * self.patch_h * self.patch_w, embed_dim, bias=bias)
        self.in_channels = in_channels
        self.embed_dim = embed_dim

    def forward(self, latent):
        x = rearrange(latent, 'b c (h p1) (w p2) -> b (h w) (c p1 p2)', p1=self.patch_h, p2=self.patch_w)
        x = self.proj(x)
        return x

class FinalLayer(nn.Module):
    """Final layer with configurable patch_size support"""

    def __init__(self, hidden_size, out_channels=8, patch_size=1):
        super().__init__()
        self.patch_h, self.patch_w = patch_size

        self.linear = nn.Linear(hidden_size, out_channels * self.patch_h * self.patch_w, bias=True)
        self.out_channels = out_channels
        self.patch_size = patch_size

    def forward(self, x, target_height, target_width):

        x = self.linear(x)

        x = rearrange(x, 'b (h w) (c p1 p2) -> b c (h p1) (w p2)',
                        h=target_height, w=target_width,
                        p1=self.patch_h, p2=self.patch_w, c=self.out_channels)
        return x

class TimestepEmbedder(nn.Module):

    def __init__(self, hidden_size, frequency_embedding_size=256):
        super().__init__()
        self.mlp = nn.Sequential(
            nn.Linear(frequency_embedding_size, hidden_size, bias=True),
            nn.SiLU(),
            nn.Linear(hidden_size, hidden_size, bias=True),
        )
        self.frequency_embedding_size = frequency_embedding_size

    @staticmethod
    def timestep_embedding(t, dim, max_period=10):
        half = dim // 2
        freqs = torch.exp(
            -math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32, device=t.device) / half
        )
        args = t[..., None].float() * freqs[None, ...]
        embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1)
        if dim % 2:
            embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1)
        return embedding

    def forward(self, t):
        t_freq = self.timestep_embedding(t, self.frequency_embedding_size)
        t_emb = self.mlp(t_freq)
        return t_emb

class RMSNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-6):
        super().__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.variance_epsilon = eps

    def forward(self, hidden_states):
        input_dtype = hidden_states.dtype
        hidden_states = hidden_states.to(torch.float32)
        variance = hidden_states.pow(2).mean(-1, keepdim=True)
        hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
        return self.weight * hidden_states.to(input_dtype)

class FeedForward(nn.Module):
    def __init__(
        self,
        dim: int,
        hidden_dim: int,
    ):
        super().__init__()
        hidden_dim = int(2 * hidden_dim / 3)
        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
    def forward(self, x):
        x =  self.w2(torch.nn.functional.silu(self.w1(x)) * self.w3(x))
        return x

def precompute_freqs_cis_2d(dim: int, height: int, width: int, theta: float = 10000.0, scale=1.0):

    if isinstance(scale, float):
        scale = (scale, scale)
    x_pos = torch.linspace(0, width * scale[0], width)
    y_pos = torch.linspace(0, height * scale[1], height)
    y_pos, x_pos = torch.meshgrid(y_pos, x_pos, indexing="ij")
    y_pos = y_pos.reshape(-1)
    x_pos = x_pos.reshape(-1)
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 4)[: (dim // 4)].float() / dim)) 
    x_freqs = torch.outer(x_pos, freqs).float() 
    y_freqs = torch.outer(y_pos, freqs).float() 
    x_cis = torch.polar(torch.ones_like(x_freqs), x_freqs)
    y_cis = torch.polar(torch.ones_like(y_freqs), y_freqs)
    freqs_cis = torch.cat([x_cis.unsqueeze(dim=-1), y_cis.unsqueeze(dim=-1)], dim=-1) 
    freqs_cis = freqs_cis.reshape(height * width, -1)
    return freqs_cis

@torch.compiler.disable
def apply_rotary_emb_2d(
        xq: torch.Tensor,
        xk: torch.Tensor,
        freqs_cis: torch.Tensor,
) -> Tuple[torch.Tensor, torch.Tensor]:

    freqs_cis = freqs_cis[None, None, :, :]  

    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) 
    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
    xq_out = torch.view_as_real(xq_ * freqs_cis).flatten(3) 
    xk_out = torch.view_as_real(xk_ * freqs_cis).flatten(3)
    return xq_out.type_as(xq), xk_out.type_as(xk)

class RAttention(nn.Module):
    def __init__(
            self,
            dim: int,
            num_heads: int = 8,
            qkv_bias: bool = False,
            qk_norm: bool = True,
            attn_drop: float = 0.,
            proj_drop: float = 0.,
            norm_layer: nn.Module = RMSNorm,
    ) -> None:
        super().__init__()
        assert dim % num_heads == 0, 'dim should be divisible by num_heads'

        self.dim = dim
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim ** -0.5

        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
        self.q_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.k_norm = norm_layer(self.head_dim) if qk_norm else nn.Identity()
        self.attn_drop = nn.Dropout(attn_drop)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x: torch.Tensor, pos, mask) -> torch.Tensor:
        B, N, C = x.shape

        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
        q, k, v = qkv[0], qkv[1], qkv[2]
        q = self.q_norm(q.contiguous())
        k = self.k_norm(k.contiguous())
        q, k = apply_rotary_emb_2d(q, k, freqs_cis=pos)

        q = q.view(B, self.num_heads, -1, C // self.num_heads)
        k = k.view(B, self.num_heads, -1, C // self.num_heads).contiguous()
        v = v.view(B, self.num_heads, -1, C // self.num_heads).contiguous()

        x = scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=self.attn_drop.p if self.training else 0.0)

        x = x.transpose(1, 2).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x

class CrossAttention(nn.Module):
    def __init__(
        self,
        dim: int,
        context_dim: int,
        num_heads: int,
        qkv_bias: bool = False,
        proj_drop: float = 0.0,
    ):
        super().__init__()
        self.num_heads = num_heads
        self.head_dim = dim // num_heads
        self.scale = self.head_dim**-0.5

        self.q_proj = nn.Linear(dim, dim, bias=qkv_bias)
        self.kv_proj = nn.Linear(context_dim, dim * 2, bias=qkv_bias)
        self.proj = nn.Linear(dim, dim)
        self.proj_drop = nn.Dropout(proj_drop)

    def forward(self, x: torch.Tensor, context: torch.Tensor, context_mask: torch.Tensor = None) -> torch.Tensor:
        B, N, C = x.shape
        B_ctx, M, C_ctx = context.shape

        q = self.q_proj(x).reshape(B, N, self.num_heads, self.head_dim).permute(0, 2, 1, 3)
        kv = self.kv_proj(context).reshape(B_ctx, M, 2, self.num_heads, self.head_dim).permute(2, 0, 3, 1, 4)
        k, v = kv[0], kv[1]

        attn_mask = None
        if context_mask is not None:
            attn_mask = torch.zeros(B, 1, 1, M, dtype=q.dtype, device=q.device)
            attn_mask.masked_fill_(~context_mask.unsqueeze(1).unsqueeze(2), float('-inf'))

        attn = scaled_dot_product_attention(q, k, v, attn_mask=attn_mask, dropout_p=self.proj_drop.p if self.training else 0.0)

        x = attn.permute(0, 2, 1, 3).reshape(B, N, C)
        x = self.proj(x)
        x = self.proj_drop(x)
        return x


class DDTBlock(nn.Module):
    def __init__(self, hidden_size, groups, mlp_ratio=4.0, context_dim=None, is_encoder_block=False):
        super().__init__()
        self.hidden_size = hidden_size
        self.norm1 = RMSNorm(hidden_size, eps=1e-6)
        self.attn = RAttention(hidden_size, num_heads=groups, qkv_bias=False)
        
        self.norm_cross = RMSNorm(hidden_size, eps=1e-6) if context_dim else nn.Identity()
        self.cross_attn = CrossAttention(hidden_size, context_dim, groups) if context_dim else None
        
        self.norm2 = RMSNorm(hidden_size, eps=1e-6)
        mlp_hidden_dim = int(hidden_size * mlp_ratio)
        self.mlp = FeedForward(hidden_size, mlp_hidden_dim)
        
        self.is_encoder_block = is_encoder_block
        if not is_encoder_block:
            self.adaLN_modulation = nn.Sequential(
                nn.Linear(hidden_size, 6 * hidden_size, bias=True)
            )

    def forward(self, x, c, pos, mask=None, context=None, context_mask=None, shared_adaLN=None):
        if self.is_encoder_block:
            adaLN_output = shared_adaLN(c)
        else:
            adaLN_output = self.adaLN_modulation(c)

        shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = adaLN_output.chunk(6, dim=-1)

        x = x + gate_msa * self.attn(modulate(self.norm1(x), shift_msa, scale_msa), pos, mask=mask)

        if self.cross_attn is not None and context is not None:
            x = x + self.cross_attn(self.norm_cross(x), context=context, context_mask=context_mask)

        x = x + gate_mlp * self.mlp(modulate(self.norm2(x), shift_mlp, scale_mlp))
        return x

class LocalSongModel(nn.Module):
    def __init__(
            self,
            in_channels=8, 
            num_groups=16,
            hidden_size=1024,  
            decoder_hidden_size=2048,  
            num_blocks=36,
            patch_size=(16,1),  
            num_classes=2304,  
            max_tags=8,  
    ):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = in_channels
        self.hidden_size = hidden_size 
        self.decoder_hidden_size = decoder_hidden_size
        self.num_groups = num_groups
        self.num_groups = num_groups
        self.num_blocks = num_blocks
        self.patch_size = patch_size
        self.num_classes = num_classes
        self.max_tags = max_tags

        self.patch_h, self.patch_w = patch_size

        self.x_embedder = PatchEmbed(
            in_channels=in_channels,
            embed_dim=decoder_hidden_size,
            bias=True,
            patch_size=patch_size
        )

        self.s_embedder = PatchEmbed(
            in_channels=in_channels,
            embed_dim=decoder_hidden_size,
            bias=True,
            patch_size=patch_size
        )

        self.encoder_to_decoder = nn.Linear(hidden_size, decoder_hidden_size, bias=False)

        self.a_to_b_proj = nn.Linear(decoder_hidden_size, hidden_size, bias=False)

        self.t_embedder = TimestepEmbedder(hidden_size)

        self.y_embedder = nn.Embedding(num_classes + 1, hidden_size, padding_idx=0)

        self.final_layer = FinalLayer(
            decoder_hidden_size,
            out_channels=in_channels,
            patch_size=patch_size
        )

        self.shared_encoder_adaLN = nn.Sequential(
            nn.Linear(hidden_size, 6 * hidden_size, bias=True)
        )

        self.shared_decoder_adaLN = nn.Sequential(
            nn.Linear(hidden_size, 6 * decoder_hidden_size, bias=True)
        )

        self.blocks = nn.ModuleList()
        for i in range(self.num_blocks):
            is_encoder = i < self.num_blocks

            if is_encoder:
                if i < 1:
                    block_hidden_size = decoder_hidden_size
                    num_heads = self.num_groups
                elif i >= self.num_blocks - 3:
                    block_hidden_size = decoder_hidden_size
                    num_heads = self.num_groups
                else:
                    block_hidden_size = hidden_size
                    num_heads = self.num_groups
            else:
                block_hidden_size = decoder_hidden_size
                num_heads = self.num_groups

            context_dim = hidden_size if i % 2 == 0 and is_encoder else None

            self.blocks.append(
                DDTBlock(
                    block_hidden_size,
                    num_heads,
                    context_dim=context_dim,
                    is_encoder_block=is_encoder
                )
            )

        self.bc_projection = nn.Linear(decoder_hidden_size + hidden_size, decoder_hidden_size, bias=False)

        self.initialize_weights()
        self.precompute_encoder_pos = dict()
        self.precompute_decoder_pos = dict()

    from functools import lru_cache

    @lru_cache
    def fetch_encoder_pos(self, height, width, device):
        key = (height, width)
        if key in self.precompute_encoder_pos:
            return self.precompute_encoder_pos[key].to(device)
        else:
            pos = precompute_freqs_cis_2d(self.hidden_size // self.num_groups, height, width).to(device)
            self.precompute_encoder_pos[key] = pos
            return pos

    @lru_cache
    def fetch_decoder_pos(self, height, width, device):
        key = (height, width)
        if key in self.precompute_decoder_pos:
            return self.precompute_decoder_pos[key].to(device)
        else:
            pos = precompute_freqs_cis_2d(self.decoder_hidden_size // self.num_groups, height, width).to(device)
            self.precompute_decoder_pos[key] = pos
            return pos

    def initialize_weights(self):
        for embedder in [self.x_embedder, self.s_embedder]:
            nn.init.xavier_uniform_(embedder.proj.weight)
            if embedder.proj.bias is not None:
                nn.init.constant_(embedder.proj.bias, 0)

        nn.init.xavier_uniform_(self.encoder_to_decoder.weight)
        nn.init.xavier_uniform_(self.a_to_b_proj.weight)

        nn.init.normal_(self.y_embedder.weight, std=0.02)

        with torch.no_grad():
            self.y_embedder.weight[0].fill_(0)

        nn.init.normal_(self.t_embedder.mlp[0].weight, std=0.02)
        nn.init.normal_(self.t_embedder.mlp[2].weight, std=0.02)

        nn.init.constant_(self.shared_encoder_adaLN[-1].weight, 0)
        nn.init.constant_(self.shared_encoder_adaLN[-1].bias, 0)
        nn.init.constant_(self.shared_decoder_adaLN[-1].weight, 0)
        nn.init.constant_(self.shared_decoder_adaLN[-1].bias, 0)

        nn.init.constant_(self.final_layer.linear.weight, 0)
        nn.init.constant_(self.final_layer.linear.bias, 0)

        nn.init.xavier_uniform_(self.bc_projection.weight)

    def embed_condition(self, cond):

        device = self.y_embedder.weight.device

        max_len = self.max_tags
        batch_size = len(cond)

        padded_tags = torch.zeros(batch_size, max_len, dtype=torch.long, device=device)

        for i, tags in enumerate(cond):
            truncated_tags = tags[:max_len]
            padded_tags[i, :len(truncated_tags)] = torch.tensor(truncated_tags, dtype=torch.long, device=device)

        padding_mask = (padded_tags != 0)

        embedded = self.y_embedder(padded_tags)

        return embedded, padding_mask

    def forward(self, x, t, y):
        y_emb, padding_mask = self.embed_condition(y)

        return self.forward_emb(x, t, y_emb, padding_mask)

    @torch.compile()
    def forward_emb(self, x, t, y_emb, padding_mask=None):
        B, _, H, W = x.shape

        h_patches = H // self.patch_h
        w_patches = W // self.patch_w
        encoder_pos = self.fetch_encoder_pos(h_patches, w_patches, x.device)
        decoder_pos = self.fetch_decoder_pos(h_patches, w_patches, x.device)

        t_emb = self.t_embedder(t.view(-1)).view(B, 1, self.hidden_size)

        t_cond = nn.functional.silu(t_emb)

        s = self.s_embedder(x) 

        s_section_a = s
        for i in range(min(1, self.num_blocks)):
            block_context = y_emb if i % 2 == 0 else None
            s_section_a = self.blocks[i](s_section_a, t_cond, decoder_pos, None, context=block_context, context_mask=padding_mask, shared_adaLN=self.shared_decoder_adaLN)

        s_section_a_projected = self.a_to_b_proj(s_section_a) 

        s_section_b = s_section_a_projected

        for i in range(1, self.num_blocks - 3):
            block_context = y_emb if i % 2 == 0 else None
            s_section_b = self.blocks[i](s_section_b, t_cond, encoder_pos, None, context=block_context, context_mask=padding_mask, shared_adaLN=self.shared_encoder_adaLN)

        s_concat = torch.cat([s_section_a, s_section_b], dim=-1)

        s = self.bc_projection(s_concat)

        for i in range(max(1, self.num_blocks - 3), self.num_blocks):
            block_context = y_emb if i % 2 == 0 else None
            s = self.blocks[i](s, t_cond, decoder_pos, None, context=block_context, context_mask=padding_mask, shared_adaLN=self.shared_decoder_adaLN)

        s = self.final_layer(s, H // self.patch_h, W // self.patch_w)

        return s