"""
FlowMatchRelay model — HuggingFace compatible.

Usage:
    from transformers import AutoModel
    model = AutoModel.from_pretrained(
        "AbstractPhil/geolip-diffusion-proto",
        trust_remote_code=True
    )

    # Generate samples
    samples = model.sample(n_samples=8, class_label=3)  # 8 cats
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from transformers import PreTrainedModel
from .configuration_flow_match import FlowMatchRelayConfig


# ══════════════════════════════════════════════════════════════════
# CONSTELLATION RELAY
# ══════════════════════════════════════════════════════════════════

class ConstellationRelay(nn.Module):
    """
    Geometric regulator for feature maps.
    Fixed anchors on S^(d-1), multi-phase stroboscope triangulation,
    gated residual correction.
    """
    def __init__(self, channels, patch_dim=16, n_anchors=16, n_phases=3,
                 pw_hidden=32, gate_init=-3.0, mode='channel'):
        super().__init__()
        assert channels % patch_dim == 0
        self.channels = channels
        self.patch_dim = patch_dim
        self.n_patches = channels // patch_dim
        self.n_anchors = n_anchors
        self.n_phases = n_phases
        self.mode = mode

        P, A, d = self.n_patches, n_anchors, patch_dim

        home = torch.empty(P, A, d)
        nn.init.xavier_normal_(home.view(P * A, d))
        home = F.normalize(home.view(P, A, d), dim=-1)
        self.register_buffer('home', home)
        self.anchors = nn.Parameter(home.clone())

        tri_dim = n_phases * A
        self.pw_w1 = nn.Parameter(torch.empty(P, tri_dim, pw_hidden))
        self.pw_b1 = nn.Parameter(torch.zeros(1, P, pw_hidden))
        self.pw_w2 = nn.Parameter(torch.empty(P, pw_hidden, d))
        self.pw_b2 = nn.Parameter(torch.zeros(1, P, d))
        for p in range(P):
            nn.init.xavier_normal_(self.pw_w1.data[p])
            nn.init.xavier_normal_(self.pw_w2.data[p])
        self.pw_norm = nn.LayerNorm(d)
        self.gates = nn.Parameter(torch.full((P,), gate_init))
        self.norm = nn.LayerNorm(channels)

    def drift(self):
        h, c = F.normalize(self.home, dim=-1), F.normalize(self.anchors, dim=-1)
        return torch.acos((h * c).sum(-1).clamp(-1 + 1e-7, 1 - 1e-7))

    def at_phase(self, t):
        h, c = F.normalize(self.home, dim=-1), F.normalize(self.anchors, dim=-1)
        omega = self.drift().unsqueeze(-1)
        so = omega.sin().clamp(min=1e-7)
        return torch.sin((1-t)*omega)/so * h + torch.sin(t*omega)/so * c

    def _relay_core(self, x_flat):
        N, C = x_flat.shape
        P, A, d = self.n_patches, self.n_anchors, self.patch_dim
        x_n = self.norm(x_flat)
        patches = x_n.reshape(N, P, d)
        patches_n = F.normalize(patches, dim=-1)
        phases = torch.linspace(0, 1, self.n_phases, device=x_flat.device).tolist()
        tris = []
        for t in phases:
            at = F.normalize(self.at_phase(t), dim=-1)
            tris.append(1.0 - torch.einsum('npd,pad->npa', patches_n, at))
        tri = torch.cat(tris, dim=-1)
        h = F.gelu(torch.einsum('npt,pth->nph', tri, self.pw_w1) + self.pw_b1)
        pw = self.pw_norm(torch.einsum('nph,phd->npd', h, self.pw_w2) + self.pw_b2)
        g = self.gates.sigmoid().unsqueeze(0).unsqueeze(-1)
        blended = g * pw + (1-g) * patches
        return x_flat + blended.reshape(N, C)

    def forward(self, x):
        B, C, H, W = x.shape
        if self.mode == 'channel':
            pooled = x.mean(dim=(-2, -1))
            relayed = self._relay_core(pooled)
            scale = (relayed / (pooled + 1e-8)).unsqueeze(-1).unsqueeze(-1)
            return x * scale.clamp(-3, 3)
        else:
            x_flat = x.permute(0, 2, 3, 1).reshape(B * H * W, C)
            out = self._relay_core(x_flat)
            return out.reshape(B, H, W, C).permute(0, 3, 1, 2)


# ══════════════════════════════════════════════════════════════════
# BUILDING BLOCKS
# ══════════════════════════════════════════════════════════════════

class SinusoidalPosEmb(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, t):
        half = self.dim // 2
        emb = math.log(10000) / (half - 1)
        emb = torch.exp(torch.arange(half, device=t.device, dtype=t.dtype) * -emb)
        emb = t.unsqueeze(-1) * emb.unsqueeze(0)
        return torch.cat([emb.sin(), emb.cos()], dim=-1)


class AdaGroupNorm(nn.Module):
    def __init__(self, channels, cond_dim, n_groups=8):
        super().__init__()
        self.gn = nn.GroupNorm(min(n_groups, channels), channels, affine=False)
        self.proj = nn.Linear(cond_dim, channels * 2)
        nn.init.zeros_(self.proj.weight)
        nn.init.zeros_(self.proj.bias)

    def forward(self, x, cond):
        x = self.gn(x)
        scale, shift = self.proj(cond).unsqueeze(-1).unsqueeze(-1).chunk(2, dim=1)
        return x * (1 + scale) + shift


class ConvBlock(nn.Module):
    def __init__(self, channels, cond_dim, use_relay=False,
                 relay_patch_dim=16, relay_n_anchors=16, relay_n_phases=3,
                 relay_pw_hidden=32, relay_gate_init=-3.0, relay_mode='channel'):
        super().__init__()
        self.dw_conv = nn.Conv2d(channels, channels, 7, padding=3, groups=channels)
        self.norm = AdaGroupNorm(channels, cond_dim)
        self.pw1 = nn.Conv2d(channels, channels * 4, 1)
        self.pw2 = nn.Conv2d(channels * 4, channels, 1)
        self.act = nn.GELU()
        self.relay = ConstellationRelay(
            channels,
            patch_dim=min(relay_patch_dim, channels),
            n_anchors=min(relay_n_anchors, channels),
            n_phases=relay_n_phases,
            pw_hidden=relay_pw_hidden,
            gate_init=relay_gate_init,
            mode=relay_mode) if use_relay else None

    def forward(self, x, cond):
        residual = x
        x = self.dw_conv(x)
        x = self.norm(x, cond)
        x = self.pw1(x)
        x = self.act(x)
        x = self.pw2(x)
        x = residual + x
        if self.relay is not None:
            x = self.relay(x)
        return x


class SelfAttnBlock(nn.Module):
    def __init__(self, channels, n_heads=4):
        super().__init__()
        self.n_heads = n_heads
        self.head_dim = channels // n_heads
        self.norm = nn.GroupNorm(8, channels)
        self.qkv = nn.Conv2d(channels, channels * 3, 1)
        self.out = nn.Conv2d(channels, channels, 1)
        nn.init.zeros_(self.out.weight)
        nn.init.zeros_(self.out.bias)

    def forward(self, x):
        B, C, H, W = x.shape
        residual = x
        x = self.norm(x)
        qkv = self.qkv(x).reshape(B, 3, self.n_heads, self.head_dim, H * W)
        q, k, v = qkv[:, 0], qkv[:, 1], qkv[:, 2]
        attn = F.scaled_dot_product_attention(q, k, v)
        out = attn.reshape(B, C, H, W)
        return residual + self.out(out)


class Downsample(nn.Module):
    def __init__(self, channels):
        super().__init__()
        self.conv = nn.Conv2d(channels, channels, 3, stride=2, padding=1)

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


class Upsample(nn.Module):
    def __init__(self, channels):
        super().__init__()
        self.conv = nn.Conv2d(channels, channels, 3, padding=1)

    def forward(self, x):
        x = F.interpolate(x, scale_factor=2, mode='nearest')
        return self.conv(x)


# ══════════════════════════════════════════════════════════════════
# FLOW MATCHING UNET
# ══════════════════════════════════════════════════════════════════

class FlowMatchUNet(nn.Module):
    def __init__(self, config):
        super().__init__()
        in_channels = config.in_channels
        base_channels = config.base_channels
        channel_mults = config.channel_mults
        n_classes = config.n_classes
        cond_dim = config.cond_dim
        use_relay = config.use_relay
        self.channel_mults = channel_mults

        # Relay kwargs
        rk = dict(
            relay_patch_dim=config.relay_patch_dim,
            relay_n_anchors=config.relay_n_anchors,
            relay_n_phases=config.relay_n_phases,
            relay_pw_hidden=config.relay_pw_hidden,
            relay_gate_init=config.relay_gate_init,
            relay_mode=config.relay_mode,
        )

        self.time_emb = nn.Sequential(
            SinusoidalPosEmb(cond_dim),
            nn.Linear(cond_dim, cond_dim), nn.GELU(),
            nn.Linear(cond_dim, cond_dim))
        self.class_emb = nn.Embedding(n_classes, cond_dim)
        self.in_conv = nn.Conv2d(in_channels, base_channels, 3, padding=1)

        # Encoder
        self.enc = nn.ModuleList()
        self.enc_down = nn.ModuleList()
        ch_in = base_channels
        enc_channels = [base_channels]

        for i, mult in enumerate(channel_mults):
            ch_out = base_channels * mult
            self.enc.append(nn.ModuleList([
                ConvBlock(ch_in, cond_dim) if ch_in == ch_out
                else nn.Sequential(nn.Conv2d(ch_in, ch_out, 1),
                                   ConvBlock(ch_out, cond_dim)),
                ConvBlock(ch_out, cond_dim),
            ]))
            ch_in = ch_out
            enc_channels.append(ch_out)
            if i < len(channel_mults) - 1:
                self.enc_down.append(Downsample(ch_out))

        # Middle
        mid_ch = ch_in
        self.mid_block1 = ConvBlock(mid_ch, cond_dim, use_relay=use_relay, **rk)
        self.mid_attn = SelfAttnBlock(mid_ch, n_heads=4)
        self.mid_block2 = ConvBlock(mid_ch, cond_dim, use_relay=use_relay, **rk)

        # Decoder
        self.dec_up = nn.ModuleList()
        self.dec_skip_proj = nn.ModuleList()
        self.dec = nn.ModuleList()

        for i in range(len(channel_mults) - 1, -1, -1):
            ch_out = base_channels * channel_mults[i]
            skip_ch = enc_channels.pop()
            self.dec_skip_proj.append(nn.Conv2d(ch_in + skip_ch, ch_out, 1))
            self.dec.append(nn.ModuleList([
                ConvBlock(ch_out, cond_dim),
                ConvBlock(ch_out, cond_dim),
            ]))
            ch_in = ch_out
            if i > 0:
                self.dec_up.append(Upsample(ch_out))

        self.out_norm = nn.GroupNorm(8, ch_in)
        self.out_conv = nn.Conv2d(ch_in, in_channels, 3, padding=1)
        nn.init.zeros_(self.out_conv.weight)
        nn.init.zeros_(self.out_conv.bias)

    def forward(self, x, t, class_labels):
        cond = self.time_emb(t) + self.class_emb(class_labels)
        h = self.in_conv(x)
        skips = [h]

        for i in range(len(self.channel_mults)):
            for block in self.enc[i]:
                if isinstance(block, ConvBlock):
                    h = block(h, cond)
                elif isinstance(block, nn.Sequential):
                    h = block[0](h)
                    h = block[1](h, cond)
                else:
                    h = block(h)
            skips.append(h)
            if i < len(self.enc_down):
                h = self.enc_down[i](h)

        h = self.mid_block1(h, cond)
        h = self.mid_attn(h)
        h = self.mid_block2(h, cond)

        for i in range(len(self.channel_mults)):
            skip = skips.pop()
            if i > 0:
                h = self.dec_up[i - 1](h)
            h = torch.cat([h, skip], dim=1)
            h = self.dec_skip_proj[i](h)
            for block in self.dec[i]:
                h = block(h, cond)

        h = self.out_norm(h)
        h = F.silu(h)
        return self.out_conv(h)


# ══════════════════════════════════════════════════════════════════
# HUGGINGFACE PRETRAINED MODEL WRAPPER
# ══════════════════════════════════════════════════════════════════

class FlowMatchRelayModel(PreTrainedModel):
    """
    HuggingFace-compatible wrapper for flow matching with constellation relay.

    Load:
        model = AutoModel.from_pretrained(
            "AbstractPhil/geolip-diffusion-proto", trust_remote_code=True)

    Generate:
        images = model.sample(n_samples=8, class_label=3)
    """
    config_class = FlowMatchRelayConfig
    _tied_weights_keys = []
    _keys_to_ignore_on_load_missing = []
    _keys_to_ignore_on_load_unexpected = []
    _no_split_modules = []
    supports_gradient_checkpointing = False

    def __init__(self, config):
        super().__init__(config)
        self.unet = FlowMatchUNet(config)
        self.post_init()

    def _init_weights(self, module):
        """No-op — weights loaded from checkpoint or already initialized."""
        pass

    def forward(self, x, t, class_labels):
        """
        Predict velocity field for flow matching.

        Args:
            x: (B, 3, H, W) noisy images
            t: (B,) timesteps in [0, 1]
            class_labels: (B,) integer class labels

        Returns:
            v_pred: (B, 3, H, W) predicted velocity
        """
        return self.unet(x, t, class_labels)

    @torch.no_grad()
    def sample(self, n_samples=8, n_steps=None, class_label=None, device=None):
        """
        Generate images via Euler ODE integration.

        Args:
            n_samples: number of images to generate
            n_steps: ODE integration steps (default from config)
            class_label: optional class conditioning (0-9 for CIFAR-10)
            device: target device

        Returns:
            images: (n_samples, 3, 32, 32) in [0, 1]
        """
        if device is None:
            device = next(self.parameters()).device
        if n_steps is None:
            n_steps = self.config.n_sample_steps

        self.eval()
        x = torch.randn(n_samples, self.config.in_channels,
                        self.config.image_size, self.config.image_size,
                        device=device)

        if class_label is not None:
            labels = torch.full((n_samples,), class_label,
                               dtype=torch.long, device=device)
        else:
            labels = torch.randint(0, self.config.n_classes,
                                  (n_samples,), device=device)

        dt = 1.0 / n_steps
        for step in range(n_steps):
            t_val = 1.0 - step * dt
            t = torch.full((n_samples,), t_val, device=device)
            v = self.unet(x, t, labels)
            x = x - v * dt

        # [-1, 1] → [0, 1]
        return (x.clamp(-1, 1) + 1) / 2

    def get_relay_diagnostics(self):
        """Report constellation relay drift and gate values."""
        diagnostics = {}
        for name, module in self.named_modules():
            if isinstance(module, ConstellationRelay):
                drift = module.drift().mean().item()
                gate = module.gates.sigmoid().mean().item()
                diagnostics[name] = {
                    'drift_rad': drift,
                    'drift_deg': math.degrees(drift),
                    'gate': gate,
                }
        return diagnostics