# Copyright 2025 Ant Group Co., Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Example: Secure Permutation and Homomorphic Aggregation.

This example demonstrates two advanced MPC/HE capabilities:
1. **Secure Permutation Network**: Using OT to shuffle data without revealing the permutation.
2. **Homomorphic Aggregation**: Using BFV Rotation to efficiently aggregate packed data.

"""

import jax.numpy as jnp
import numpy as np

import mplang.v2 as mp
from mplang.v2.dialects import bfv, simp, tensor
from mplang.v2.libs.mpc.analytics import apply_permutation, rotate_and_sum


def main():
    # Initialize Simulator (2 Parties)
    # Party 0: Data Owner
    # Party 1: Compute Node
    interp = mp.make_simulator(2)

    print("=== Part 1: Secure Permutation Network ===")
    run_secure_permutation(interp)

    print("\n=== Part 2: Homomorphic Aggregation (BFV) ===")
    run_homomorphic_aggregation(interp)


def run_secure_permutation(interp):
    """Demonstrate shuffling data using OT."""

    # Data on Party 0
    # Permutation on Party 1: [2, 0, 1, 3] -> Expect [30, 10, 20, 40]
    # Note: Our simple permutation lib currently only supports N=2 or manual construction.
    # For this demo, we will use N=2 to show it works, or manually chain switches for N=4?
    # The `apply_permutation` in lib currently raises NotImplemented for N=4.
    # Let's stick to N=2 for the automated call, or implement the N=4 logic manually here.

    # Let's use N=2 for simplicity of the library call.
    data_n2 = [10, 20]
    perm_n2 = [1, 0]  # Swap

    print(f"Sender (P0) Data: {data_n2}")
    print(f"Receiver (P1) Permutation: {perm_n2}")

    def protocol(d0, d1, p0, p1):
        # Construct permutation tensor on P1
        def make_perm(a, b):
            return tensor.run_jax(lambda x, y: jnp.array([x, y]), a, b)

        perm = simp.pcall_static((1,), make_perm, p0, p1)

        # Run secure permutation
        return apply_permutation([d0, d1], perm, sender=0, receiver=1)

    with interp:
        d0_obj = simp.constant((0,), data_n2[0])
        d1_obj = simp.constant((0,), data_n2[1])
        p0_obj = simp.constant((1,), perm_n2[0])
        p1_obj = simp.constant((1,), perm_n2[1])

        res = protocol(d0_obj, d1_obj, p0_obj, p1_obj)

    # Result is on P1
    res_vals = [r.runtime_obj.values[1] for r in res]
    print(f"Receiver (P1) Result: {res_vals}")

    expected = [20, 10]
    assert res_vals == expected
    print("Secure Permutation Verified!")


def run_homomorphic_aggregation(interp):
    """Demonstrate O(log N) aggregation using BFV."""

    # Setup BFV context (Keys generated by P0)
    # In this simulation, we treat BFV ops as local or "available to all who have keys".
    # We'll simulate P0 generating keys and P1 computing.

    # 1. Keygen (P0)
    pk, sk = bfv.keygen(poly_modulus_degree=4096)
    gk = bfv.make_galois_keys(sk)
    encoder = bfv.create_encoder(poly_modulus_degree=4096)

    # 2. Data (P0)
    # Vector of size 8: [1, 1, 1, 1, 1, 1, 1, 1] -> Sum = 8
    data = np.array([1] * 8 + [0] * 4088, dtype=np.int64)

    t_data = tensor.constant(data)
    pt = bfv.encode(t_data, encoder)
    ct = bfv.encrypt(pt, pk)

    print(f"Encrypted Data (First 8 slots): {data[:8]}")

    # 3. Aggregation (P1)
    # P1 has ct and gk. P1 computes sum of first 8 elements.

    def compute(ct_in, gk_in):
        return rotate_and_sum(ct_in, k=8, galois_keys=gk_in)

    res_ct = compute(ct, gk)

    # 4. Decrypt (P0)
    res_pt = bfv.decrypt(res_ct, sk)
    res_tensor = bfv.decode(res_pt, encoder)

    # In simulation, we can access the value directly
    result_val = res_tensor.runtime_obj[0]
    print(f"Aggregated Result (Slot 0): {result_val}")

    assert result_val == 8
    print("Homomorphic Aggregation Verified!")


if __name__ == "__main__":
    main()
