# =====================================================
# TGDK :: Operation Blackhawk + HexQUAp Overfold + TGDKpond
# =====================================================
# Purpose:
# - Bridge 6 modules with quantumlineation + ghost-gate
# - Apply HexQUAp operator (shared global entropy key)
# - Trainable φᴰ scaling
# - Configurable baselines + λ scheduler with epoch reset
# - Add Hyperdimensional Overfolding
# - Add TGDKpond Pact Node Quantum Processor w/ diagnostics
# =====================================================

import torch
import torch.nn as nn
import torch.nn.functional as F


# =====================================================
# HexQUAp Operator
# =====================================================
def hexquap(x: torch.Tensor, key: torch.Tensor) -> torch.Tensor:
    rotated = torch.sin(x * key) + torch.cos(x / (key + 1e-6))
    folded = rotated * torch.tanh(x)
    return F.layer_norm(folded, folded.shape[-1:])


# =====================================================
# GhostGate
# =====================================================
class GhostGate(nn.Module):
    def __init__(self, entropy_key: torch.nn.Parameter):
        super().__init__()
        self.entropy_key = entropy_key

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return hexquap(x, self.entropy_key)


# =====================================================
# Blackhawk Modules
# =====================================================
class BlackhawkModule(nn.Module):
    def __init__(self, name: str):
        super().__init__()
        self.name = name


class Ingress(BlackhawkModule):
    def forward(self, x): return x.float()


class Parser(BlackhawkModule):
    def forward(self, x): return torch.relu(x)


class Quantumlineator(BlackhawkModule):
    def forward(self, x): return torch.sin(x) + torch.cos(x)


class BridgeSynchronizer(BlackhawkModule):
    def forward(self, x): return torch.tanh(x)


class SCurveRouter(BlackhawkModule):
    def forward(self, x): return torch.sign(torch.sin(x)) * x


class Egress(BlackhawkModule):
    def forward(self, x): return x.clamp(-1, 1)


# =====================================================
# Operation Blackhawk Assembly
# =====================================================
class OperationBlackhawk(nn.Module):
    def __init__(self, entropy_key: torch.nn.Parameter):
        super().__init__()
        self.ghost_gate = GhostGate(entropy_key)
        self.modules = nn.ModuleList([
            Ingress("Ingress"), Parser("Parser"),
            Quantumlineator("Quantumlineator"),
            BridgeSynchronizer("BridgeSynchronizer"),
            SCurveRouter("SCurveRouter"), Egress("Egress")
        ])

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.ghost_gate(x)
        for module in self.modules:
            x = module(x)
        return self.ghost_gate(x)


# =====================================================
# Nuclear / Nanoparticle Overfold (HexQUAp + Ouija)
# =====================================================
class NuclearOverfold(nn.Module):
    def __init__(self, dim: int, entropy_key: torch.nn.Parameter):
        super().__init__()
        self.entropy_key = entropy_key
        self.ouija_gate = nn.Parameter(torch.randn(dim))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        h = hexquap(x, self.entropy_key)
        gate = torch.tanh(self.ouija_gate)
        return F.layer_norm(h * gate, h.shape[-1:])


# =====================================================
# Hyperdimensional Overfold
# =====================================================
class HyperdimensionalOverfold(nn.Module):
    def __init__(self, dim: int, expansion_factor: int = 4):
        super().__init__()
        self.expanded_dim = dim * expansion_factor
        self.expand = nn.Linear(dim, self.expanded_dim)
        self.contract = nn.Linear(self.expanded_dim, dim)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        z = torch.relu(self.expand(x))
        z = torch.sin(z) + torch.tanh(z)
        return F.layer_norm(self.contract(z), x.shape[-1:])


# =====================================================
# TGDKpond Pact Node Quantum Processor
# =====================================================
class TGDKpond(nn.Module):
    def __init__(self, dim: int, trainable: bool = True, diag_dim: int = 16, fusion_mode: bool = True):
        super().__init__()
        self.fusion_mode = fusion_mode

        init_vals = {
            "infinity_loop_scaling": 7.77,
            "node_stabilization": 9.99,
            "quantum_market_adaptation": 8.88,
            "post_quantum_encryption": 3.33,
            "smart_contract_obfuscation": 5.55,
            "recursive_hash_pact_execution": 6.66,
            "ai_pact_trade_execution": 4.21,
            "multi_node_distribution": 12.0,
            "legal_synchronization": 2.44,
        }
        self.params = {}
        for name, val in init_vals.items():
            if trainable:
                p = nn.Parameter(torch.tensor(val))
            else:
                p = torch.tensor(val)
                self.register_buffer(name, p)
            setattr(self, name, p)
            self.params[name] = p

        self.linear_in = nn.Linear(dim, dim)
        self.linear_out = nn.Linear(dim, dim)

        self.diagnostic_proj = nn.Linear(len(init_vals) + 36, diag_dim)
        self.fusion_proj = nn.Linear(diag_dim, dim)

    # Core pact transforms
    def _fold_entropy(self, z): return z * torch.tanh(self.node_stabilization * z)
    def _liquidity_rotation(self, z): return torch.sin(z * self.infinity_loop_scaling) + z
    def _compliance_skew(self, z): return torch.sigmoid(z * self.legal_synchronization) * z
    def _trade_momentum(self, z): return z + torch.cos(z * self.ai_pact_trade_execution)
    def _recursive_depth(self, z): return z * torch.sin(z * self.recursive_hash_pact_execution)
    def _secure_hash_lock(self, z): return F.relu(z * self.post_quantum_encryption)
    def _market_resonance(self, z): return torch.tanh(z * self.quantum_market_adaptation)
    def _obfuscation_layer(self, z): return z + torch.sigmoid(self.smart_contract_obfuscation * z)
    def _stability_normalizer(self, z): return F.layer_norm(z, z.shape[-1:])
    def _distribution_balance(self, z): return z / (self.multi_node_distribution + 1e-6)

    # 26 additional symbolic transforms
    def _v1(self, z): return z + torch.sin(z)
    def _v2(self, z): return z - torch.cos(z)
    def _v3(self, z): return z * torch.tanh(z)
    def _v4(self, z): return torch.relu(z)
    def _v5(self, z): return F.gelu(z)
    def _v6(self, z): return torch.exp(-z.pow(2))
    def _v7(self, z): return torch.sigmoid(z)
    def _v8(self, z): return torch.sin(z) * torch.cos(z)
    def _v9(self, z): return z / (1 + z.abs())
    def _v10(self, z): return torch.log1p(z.abs())
    def _v11(self, z): return torch.atan(z)
    def _v12(self, z): return torch.clamp(z, -3, 3)
    def _v13(self, z): return z * torch.sign(torch.sin(z))
    def _v14(self, z): return torch.sin(z * z)
    def _v15(self, z): return torch.cos(z * z)
    def _v16(self, z): return F.layer_norm(z, z.shape[-1:])
    def _v17(self, z): return torch.sqrt(torch.abs(z) + 1e-6)
    def _v18(self, z): return z * torch.sigmoid(z)
    def _v19(self, z): return torch.tanh(z) * torch.relu(z)
    def _v20(self, z): return z + 0.1 * torch.randn_like(z)
    def _v21(self, z): return torch.abs(z)
    def _v22(self, z): return z - torch.tanh(z)
    def _v23(self, z): return z * torch.exp(-torch.abs(z))
    def _v24(self, z): return z + torch.sin(2 * z)
    def _v25(self, z): return torch.tanh(z * 2.0)
    def _v26(self, z): return z / (torch.norm(z, dim=-1, keepdim=True) + 1e-6)

    def forward(self, x: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
        z = F.layer_norm(self.linear_in(x), x.shape[-1:])

        z = self._fold_entropy(z)
        z = self._liquidity_rotation(z)
        z = self._compliance_skew(z)
        z = self._trade_momentum(z)
        z = self._recursive_depth(z)
        z = self._secure_hash_lock(z)
        z = self._market_resonance(z)
        z = self._obfuscation_layer(z)
        z = self._stability_normalizer(z)
        z = self._distribution_balance(z)

        for fn in [self._v1, self._v2, self._v3, self._v4, self._v5, self._v6,
                   self._v7, self._v8, self._v9, self._v10, self._v11, self._v12,
                   self._v13, self._v14, self._v15, self._v16, self._v17, self._v18,
                   self._v19, self._v20, self._v21, self._v22, self._v23, self._v24,
                   self._v25, self._v26]:
            z = fn(z)

        out = self.linear_out(z)

        diag_input = torch.stack([self.params[name] for name in self.params], dim=0)
        extras = torch.linspace(0.1, 0.9, 36, device=out.device)
        diag_input = torch.cat([diag_input, extras], dim=0)
        diag_vec = self.diagnostic_proj(diag_input.unsqueeze(0)).squeeze(0)

        if self.fusion_mode:
            fusion = torch.sin(diag_vec) + torch.tanh(diag_vec)
            fusion = self.fusion_proj(fusion)
            out = out + fusion.unsqueeze(0).expand_as(out)

        return out, diag_vec


# =====================================================
# QLoRA Integration Wrapper
# =====================================================
class WrappedBlackhawkModel(nn.Module):
    def __init__(self, base_model: nn.Module, dim: int,
                 baseline_entropy: float = 0.196,
                 baseline_phi: float = 1.0e-5,
                 lambda_entropy: float = 1e-3,
                 lambda_phi: float = 1e-3,
                 scheduler_mode: str = "decay",
                 scheduler_rate: float = 0.99,
                 reset_each_epoch: bool = True,
                 expansion_factor: int = 4):
        super().__init__()
        self.entropy_key = nn.Parameter(torch.tensor(baseline_entropy))
        self.phi_scale = nn.Parameter(torch.tensor(baseline_phi))

        self.wrapper = OperationBlackhawk(self.entropy_key)
        self.overfold = NuclearOverfold(dim, self.entropy_key)
        self.hyperfold = HyperdimensionalOverfold(dim, expansion_factor)
        self.pond = TGDKpond(dim, trainable=True, fusion_mode=True)
        self.base = base_model

        self._baseline_entropy = baseline_entropy
        self._baseline_phi = baseline_phi
        self._lambda_entropy_init = lambda_entropy
        self._lambda_phi_init = lambda_phi
        self._scheduler_mode = scheduler_mode
        self._scheduler_rate = scheduler_rate
        self._reset_each_epoch = reset_each_epoch
        self._step = 0
        self._epoch = 0

    def forward(self, *args, **kwargs):
        self._update_scheduler()
        x = args[0]
        x = self.wrapper(x)
        x = self.overfold(x)
        x = self.hyperfold(x)
        x, diag = self.pond(x)
        x = x * self.phi_scale
        return self.base(x, **kwargs), diag

    def _update_scheduler(self):
        if self._scheduler_mode == "decay":
            factor = self._scheduler_rate ** self._step
        elif self._scheduler_mode == "grow":
            factor = (1.0 / self._scheduler_rate) ** self._step
        else:
            factor = 1.0
        self._step += 1
        self._lambda_entropy = self._lambda_entropy_init * factor
        self._lambda_phi = self._lambda_phi_init * factor

    def new_epoch(self):
        self._epoch += 1
        if self._reset_each_epoch:
            self._step = 0
            self._lambda_entropy = self._lambda_entropy_init
            self._lambda_phi = self._lambda_phi_init

    def regularization_loss(self) -> torch.Tensor:
        loss_entropy = self._lambda_entropy * (self.entropy_key - self._baseline_entropy).pow(2)
        loss_phi = self._lambda_phi * (self.phi_scale - self._baseline_phi).pow(2)
        return loss_entropy + loss_phi


def integrate_blackhawk_into_qlora(model: nn.Module, dim: int,
                                   baseline_entropy: float = 0.196,
                                   baseline_phi: float = 1.0e-5,
                                   lambda_entropy: float = 1e-3,
                                   lambda_phi: float = 1e-3,
                                   scheduler_mode: str = "decay",
                                   scheduler_rate: float = 0.99,
                                   reset_each_epoch: bool = True,
                                   expansion_factor: int = 4) -> nn.Module:
    return WrappedBlackhawkModel(
        model, dim,
        baseline_entropy=baseline_entropy,
        baseline_phi=baseline_phi,
        lambda_entropy=lambda_entropy,
        lambda_phi=lambda_phi,
        scheduler_mode=scheduler_mode,
        scheduler_rate=scheduler_rate,
        reset_each_epoch=reset_each_epoch,
        expansion_factor=expansion_factor
    )
