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

try:
    from .configuration_pillars import PillarsConfig
except ImportError:
    from configuration_pillars import PillarsConfig

try:
    from x_transformers import Encoder
except ImportError:
    raise ImportError("To use PILLARS, you must run: pip install x-transformers")

# --- UTILS ---
class ComplexDropout(nn.Module):
    def __init__(self, p=0.5):
        super().__init__()
        self.p = p
    def forward(self, z):
        if not self.training or self.p == 0.0: return z
        mask = torch.ones_like(z.real)
        mask = F.dropout(mask, self.p, self.training, inplace=False)
        return z * mask

class RobustPhaseNorm(nn.Module):
    def __init__(self, d_model, eps=1e-5):
        super().__init__()
        self.scale = nn.Parameter(torch.ones(d_model))
        self.eps = eps
    def forward(self, x):
        mag = torch.abs(x)
        rms = torch.sqrt(torch.mean(mag**2, dim=-1, keepdim=True) + self.eps)
        return (x / rms) * self.scale

class ModReLU(nn.Module):
    def __init__(self, features):
        super().__init__()
        self.b = nn.Parameter(torch.zeros(features))
    def forward(self, z):
        mag = torch.abs(z)
        new_mag = F.relu(mag + self.b)
        phase = z / (mag + 1e-6)
        return new_mag * phase

class ComplexToRealBridge(nn.Module):
    def __init__(self, d_model):
        super().__init__()
        self.proj = nn.Linear(d_model * 2, d_model)
        self.norm = nn.LayerNorm(d_model)
    def forward(self, x_complex):
        cat = torch.cat([x_complex.real, x_complex.imag], dim=-1)
        return self.norm(self.proj(cat))

# --- COMPONENTS ---
class DynamicRoSE(nn.Module):
    def __init__(self, num_embeddings, embedding_dim, max_period=10000.0):
        super().__init__()
        self.embedding_dim = embedding_dim
        self.raw_embedding = nn.Embedding(num_embeddings, embedding_dim)
        self.adapter = nn.Linear(embedding_dim, embedding_dim * 2)
        freqs = torch.exp(torch.arange(0, embedding_dim, dtype=torch.float32) * -(math.log(max_period) / embedding_dim))
        self.register_buffer('freqs', freqs)
        self.rotation_predictor = nn.Linear(embedding_dim, embedding_dim * 2)

    def forward(self, input_ids):
        real_base = self.raw_embedding(input_ids)
        B, L, D = real_base.shape
        complex_params = self.adapter(real_base)
        z_t = torch.complex(complex_params[..., :D], complex_params[..., D:])
        rot_raw = self.rotation_predictor(real_base)
        rot_x, rot_y = rot_raw.chunk(2, dim=-1)
        rot_mag = torch.sqrt(rot_x**2 + rot_y**2 + 1e-6)
        dynamic_rot = torch.complex(rot_x / rot_mag, rot_y / rot_mag)
        pos = torch.arange(L, device=input_ids.device).float()
        static_angles = torch.outer(pos, self.freqs)
        static_rot = torch.polar(torch.ones_like(static_angles), static_angles)
        z_final = z_t * static_rot.unsqueeze(0) * dynamic_rot
        return z_final, real_base

class HyenaNeuralFilter(nn.Module):
    def __init__(self, d_model, max_len=1024, hidden_dim=64):
        super().__init__()
        self.d_model = d_model
        freqs = torch.exp(torch.arange(0, hidden_dim, 2, dtype=torch.float32) * -(math.log(10000.0) / hidden_dim))
        self.register_buffer("freqs", freqs)
        self.mlp = nn.Sequential(
            nn.Linear(hidden_dim, hidden_dim), nn.SiLU(),
            nn.Linear(hidden_dim, hidden_dim), nn.SiLU(),
            nn.Linear(hidden_dim, d_model * 2)
        )
    def forward(self, L, device):
        t = torch.linspace(0, 1, steps=L, device=device).unsqueeze(-1)
        emb = torch.cat([torch.sin(t * self.freqs), torch.cos(t * self.freqs)], dim=-1)
        out = self.mlp(emb).view(L, self.d_model, 2)
        return torch.complex(out[..., 0], out[..., 1])

class GatedHarmonicConvolution(nn.Module):
    def __init__(self, d_model, max_len=1024, dropout=0.1):
        super().__init__()
        self.d_model = d_model
        self.filter_len = max_len
        self.neural_filter = HyenaNeuralFilter(d_model, max_len=max_len)
        self.gate_proj = nn.Linear(d_model * 2, d_model * 2)
        self.mix_real = nn.Linear(d_model, d_model)
        self.mix_imag = nn.Linear(d_model, d_model)
        self.out_real = nn.Linear(d_model, d_model)
        self.out_imag = nn.Linear(d_model, d_model)
        self.activation = ModReLU(d_model)
        self.norm = RobustPhaseNorm(d_model)
        self.dropout = ComplexDropout(dropout)

    def forward(self, x, src_mask=None):
        residual = x
        x_norm = self.norm(x)
        if src_mask is not None:
             x_norm = x_norm.masked_fill(src_mask.unsqueeze(-1), 0.0)
        B, L, D = x_norm.shape
        eff_L = min(L, self.filter_len)
        x_freq = torch.fft.fft(x_norm, n=eff_L, dim=1, norm='ortho')
        h = self.neural_filter(eff_L, x.device).unsqueeze(0)
        x_filtered = x_freq * h
        x_time = torch.fft.ifft(x_filtered, n=eff_L, dim=1, norm='ortho')
        if L > eff_L: x_time = F.pad(x_time, (0,0,0,L-eff_L))
        else: x_time = x_time[:, :L, :]
        gates = torch.sigmoid(self.gate_proj(torch.cat([x_norm.real, x_norm.imag], dim=-1)))
        g_r, g_i = gates.chunk(2, dim=-1)
        x_gated = torch.complex(x_time.real * g_r, x_time.imag * g_i)
        mr, mi = self.mix_real, self.mix_imag
        x_mixed = torch.complex(mr(x_gated.real) - mi(x_gated.imag), mr(x_gated.imag) + mi(x_gated.real))
        x_act = self.activation(x_mixed)
        or_, oi = self.out_real, self.out_imag
        out = torch.complex(or_(x_act.real) - oi(x_act.imag), or_(x_act.imag) + oi(x_act.real))
        return self.dropout(out) + residual

class PRISMEncoder(nn.Module):
    def __init__(self, num_layers, d_model, max_len, dropout=0.1):
        super().__init__()
        self.layers = nn.ModuleList([
            GatedHarmonicConvolution(d_model, max_len, dropout)
            for _ in range(num_layers)
        ])
        self.final_norm = RobustPhaseNorm(d_model)
    def forward(self, x, src_mask=None):
        for layer in self.layers:
            if self.training: x = torch.utils.checkpoint.checkpoint(layer, x, src_mask, use_reentrant=False)
            else: x = layer(x, src_mask)
        return self.final_norm(x)

class FNetBlock(nn.Module):
    def __init__(self, d_model, d_ff, dropout):
        super().__init__()
        self.norm_mix = nn.LayerNorm(d_model)
        self.norm_ff = nn.LayerNorm(d_model)
        self.mix_dropout = nn.Dropout(dropout)
        self.ff = nn.Sequential(
            nn.Linear(d_model, d_ff), nn.GELU(), nn.Dropout(dropout),
            nn.Linear(d_ff, d_model), nn.Dropout(dropout)
        )
    def forward(self, x):
        residual = x
        x = self.norm_mix(x)
        with torch.cuda.amp.autocast(enabled=False):
            x = x.float()
            x = torch.fft.fftn(x, dim=(-2, -1), norm='ortho').real
            x = x.to(dtype=residual.dtype)
        x = self.mix_dropout(x)
        x = x + residual
        residual = x
        x = self.norm_ff(x)
        x = self.ff(x)
        return x + residual

class FNetEncoder(nn.Module):
    def __init__(self, depth, d_model, d_ff, dropout):
        super().__init__()
        self.layers = nn.ModuleList([
            FNetBlock(d_model, d_ff, dropout) for _ in range(depth)
        ])
        self.norm_out = nn.LayerNorm(d_model)
    def forward(self, x):
        for layer in self.layers:
            x = layer(x)
        return self.norm_out(x)

# --- MAIN MODEL ---
class PillarsModel(PreTrainedModel):
    config_class = PillarsConfig

    def __init__(self, config):
        super().__init__(config)
        self.config = config
        
        # 1. SHARED ROOT
        self.rose = DynamicRoSE(config.vocab_size, config.d_model)

        # 2. DOWNSAMPLE
        self.particle_down = nn.Linear(config.d_model, config.d_branch)
        self.wave_down = nn.Linear(config.d_model * 2, config.d_branch * 2)

        # 3. RATE STREAM (FNet)
        self.fnet_pos = nn.Embedding(config.seq_len, config.d_branch)
        self.stream_rate = FNetEncoder(depth=config.depth, d_model=config.d_branch, d_ff=config.d_branch*4, dropout=config.dropout)

        # 4. PHASE STREAM (PRISM)
        self.stream_phase = PRISMEncoder(num_layers=config.depth, d_model=config.d_branch, max_len=config.seq_len, dropout=config.dropout)
        self.phase_bridge = ComplexToRealBridge(config.d_branch)

        # 5. FUSION
        self.fusion_proj = nn.Linear(config.d_branch * 2, config.d_model)
        self.fusion_norm = nn.LayerNorm(config.d_model)

        # 6. REFINER
        self.refiner = Encoder(
            dim=config.d_model, 
            depth=config.refine_depth, 
            heads=8, 
            attn_flash=True,
            rotary_pos_emb=True, 
            attn_dropout=config.dropout, 
            ff_dropout=config.dropout
        )

        # 7. HEAD
        self.head_bias = nn.Parameter(torch.zeros(config.vocab_size))

    def forward(self, input_ids, labels=None):
        # A. Shared Root
        wave_src, particle_src = self.rose(input_ids)

        # B. Downsample
        p_small = self.particle_down(particle_src)
        w_flat = torch.cat([wave_src.real, wave_src.imag], dim=-1)
        w_small_flat = self.wave_down(w_flat)
        w_small = torch.complex(w_small_flat[..., :self.config.d_branch], w_small_flat[..., self.config.d_branch:])

        # C. Branches
        pos_emb = self.fnet_pos(torch.arange(input_ids.shape[1], device=input_ids.device))
        rate_out = self.stream_rate(p_small + pos_emb)
        phase_out = self.phase_bridge(self.stream_phase(w_small))

        # D. Fusion
        stacked = torch.cat([rate_out, phase_out], dim=-1)
        context = self.fusion_norm(self.fusion_proj(stacked))

        # E. Refiner & Output
        refined = self.refiner(context)
        # Weight tying: Use rose embeddings as output weights
        logits = F.linear(refined, self.rose.raw_embedding.weight, self.head_bias)
        
        loss = None
        if labels is not None:
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(logits.view(-1, self.config.vocab_size), labels.view(-1))
            return {"loss": loss, "logits": logits}
            
        return logits