import logging
import numpy as np
import torch
import torch.nn as nn
import hashlib, secrets, time

class ClauseStandpoint:
    def __init__(self, fallback_size=128):
        self.active_data = None
        self.fallback_size = fallback_size

    def withhold(self):
        logging.info("[ClauseStandpoint] Withholding data.")
        return None

    def install(self, data):
        logging.info("[ClauseStandpoint] Installing operator-provided data.")
        self.active_data = np.array(data)
        return self.active_data

    def fallback(self, respect=1.0, compassion=1.0, honesty_clause=1.0,
                 devaluation_metric=1.0, sympathizer=1.0):
        logging.info("[ClauseStandpoint] Generating fallback data.")
        x = np.linspace(0, 1, self.fallback_size)

        csr = (x * np.pi**(14**2)) / (
            (44 * devaluation_metric) *
            (respect * compassion * honesty_clause)
        )
        csr *= (sympathizer**2) * (440 / (self._return_to_honor(respect)))

        self.active_data = np.tanh(csr)  # bounded [-1,1]
        return self.active_data

    @staticmethod
    def _return_to_honor(respect):
        return max(respect, 1e-6)


class FractalAI:
    """
    Recursive resonance stabilizer.
    Designed as the 'heart chakra' of the system,
    feeding coherence into clause and fusion processes.
    """
    def __init__(self, depth=5):
        self.depth = depth

    def heart_chakra(self, signal: float) -> float:
        resonance = signal
        for i in range(self.depth):
            resonance = torch.sin(torch.tensor(resonance * (i+1))).item()
        return resonance


# ==============================
# Diminished Clause Vector
# ==============================
def diminished_clause_vector(tensor: torch.Tensor, factor: float = 0.5) -> torch.Tensor:
    """
    Collapse a tensor to a diminished state (attenuation).
    Useful for stabilizing over-extended fold metrics.
    """
    return tensor * factor

import numpy as np

def olivia_param_seed():
    base = 1 * 12 * 144 * 66440
    phi = (1 + 5**0.5) / 2

    # giant exponent
    exponent = np.pi ** 144

    # log10 scale for sanity
    log_params = np.log10(base) * exponent
    scaled = log_params * (3/2) * 3 * phi

    print(f"⚡ Olivia Param Seed (log10-scaled): {scaled:.4e}")
    return scaled



# 🔹 Example usage
if __name__ == "__main__":
    dummy_model = object()  # replace with fusion_model or Olivia
    standpoint = ClauseStandpoint()
    possessor = PossessorScript(dummy_model, standpoint)

    # Epoch 0: silent mode
    possessor.paradoxialize_epoch(epoch=0, state="withhold")

    # Epoch 1: operator injects their own vector
    possessor.paradoxialize_epoch(epoch=1, state="install", operator_data=[0.1, 0.5, 0.9])

    # Epoch 2: fallback to CSR synthesis
    possessor.paradoxialize_epoch(epoch=2, state="fallback")
