#!/usr/bin/env python3
"""Finite checks for the central-sector repair and noncompletion extensions.

Finite calculations are supplementary checks, not proofs or certificates of
novelty. The large tail examples are checked through exact counts and Fourier
coefficients; no 2**m by 2**m matrix is allocated for them.
"""

from __future__ import annotations

import argparse
import json
from math import comb
from pathlib import Path

import numpy as np

from verify_rigidity import nearest_phase_projection, phase_code, support_data


SEED = 20261103
TOLERANCE = 3.0e-9


def operator_norm(matrix: np.ndarray) -> float:
    return float(np.linalg.norm(matrix, ord=2))


def check_tail_construction(m: int) -> dict:
    assert m % 4 == 3
    tail_parameter = (m - 3) // 4
    low = {word for word in range(1 << m)
           if word.bit_count() % 2 == 0 and word.bit_count() <= 2 * tail_parameter}
    high = {word for word in range(1 << m)
            if word.bit_count() % 2 == 1 and word.bit_count() >= 2 * tail_parameter + 3}
    chosen = low | high
    half_dimension = 1 << (m - 1)
    expected_deficit = comb(m - 1, (m - 1) // 2)
    assert len(low) == len(high)
    assert len(chosen) == half_dimension - expected_deficit
    mask = (1 << m) - 1
    assert {word ^ mask for word in low} == high
    forbidden_edges = sum(
        ((word ^ (1 << position)) in chosen)
        for word in chosen for position in range(m)
    )
    assert forbidden_edges == 0
    # Fourier matrix elements of logical Z_j are exactly adjacency indicators:
    # <t|_X Z_j |u>_X = 1 if t = u xor 2**j, and zero otherwise.
    # The preceding exhaustive edge check therefore checks every KL entry.
    parity_sum = sum((-1) ** word.bit_count() for word in chosen)
    complement_coefficient = parity_sum / (1 << m)
    assert parity_sum == 0
    # Physical dual rails preserve every basis vector's weight m.
    physical_words = []
    for logical_word in range(1 << m):
        physical_word = 0
        for position in range(m):
            bit = (logical_word >> position) & 1
            physical_word |= bit << (2 * position)
            physical_word |= (1 - bit) << (2 * position + 1)
        assert physical_word.bit_count() == m
        physical_words.append(physical_word)
    physical_mask = (1 << (2 * m)) - 1
    physical_set = set(physical_words)
    assert all((word ^ physical_mask) in physical_set for word in physical_words)
    signs = np.array([[2 * ((word >> j) & 1) - 1 for j in range(2 * m)]
                      for word in physical_words], dtype=float)
    covariance = signs.T @ signs / (1 << m)
    expected_covariance = np.kron(np.eye(m), np.array([[1, -1], [-1, 1]]))
    covariance_error = float(np.max(np.abs(covariance - expected_covariance)))
    assert covariance_error == 0
    # Every complementary matrix entry is zero, so the general-rank nearest
    # phase formula is K - 2 sum_pairs |P[x, complement(x)]| = K.
    nearest_squared_distance = half_dimension
    return {
        "kind": "exact_tail_noncompletion",
        "logical_coordinates": m,
        "physical_coordinates": 2 * m,
        "ambient_dimension": 1 << m,
        "maximum_code_rank_K": half_dimension,
        "tail_code_rank_k": len(chosen),
        "rank_deficit": expected_deficit,
        "rank_fraction": len(chosen) / half_dimension,
        "even_count": len(low),
        "odd_count": len(high),
        "forbidden_cube_edges": forbidden_edges,
        "complement_fourier_coefficient": complement_coefficient,
        "covariance_error": covariance_error,
        "largest_covariance_eigenvalue": float(np.linalg.eigvalsh(covariance)[-1]),
        "nearest_maximal_code_squared_distance": nearest_squared_distance,
        "normalized_squared_distance": nearest_squared_distance / half_dimension,
        "check_method": "Exact tail counts, exhaustive cube-edge check, exact parity sum, and small covariance matrix; no global code projector allocated.",
    }


def check_operator_repair(n: int, projection: np.ndarray, label: str) -> dict:
    data = support_data(n, [word for word in range(1 << n) if word.bit_count() == n // 2])
    dimension, rank = data["dimension"], data["rank"]
    identity = np.eye(dimension)
    reflection = 2 * projection - identity
    eigenvalues, eigenvectors = np.linalg.eigh(projection)
    positive_space = eigenvectors[:, -rank:]
    negative_space = eigenvectors[:, :rank]
    assert np.max(np.abs(eigenvalues[:rank])) < TOLERANCE
    assert np.max(np.abs(eigenvalues[-rank:] - 1)) < TOLERANCE
    alphas, defects = [], []
    maximum_cs_spectral_error = 0.0
    maximum_operator_identity_error = 0.0
    observables = []
    for position in range(n):
        observable = np.diag(data["signs"][:, position])
        observables.append(observable)
        compression = positive_space.conj().T @ observable @ positive_space
        other_compression = negative_space.conj().T @ observable @ negative_space
        alpha = float(np.trace(compression).real / rank)
        defect = operator_norm(compression - alpha * np.eye(rank))
        residual = reflection @ observable + observable @ reflection - 2 * alpha * identity
        maximum_operator_identity_error = max(
            maximum_operator_identity_error, abs(operator_norm(residual) - 2 * defect)
        )
        maximum_cs_spectral_error = max(
            maximum_cs_spectral_error,
            float(np.max(np.abs(np.linalg.eigvalsh(other_compression) + np.linalg.eigvalsh(compression)[::-1]))),
        )
        alphas.append(alpha)
        defects.append(defect)
    epsilon = max(defects)
    alpha_bound = (n - 1) * epsilon / n
    assert max(abs(alpha) for alpha in alphas) <= alpha_bound + TOLERANCE
    assert abs(sum(alphas)) < TOLERANCE
    assert maximum_operator_identity_error < TOLERANCE
    assert maximum_cs_spectral_error < TOLERANCE
    filtered = reflection.copy()
    maximum_step_excess = 0.0
    for position in range(n - 1):
        next_filtered = (filtered - observables[position] @ filtered @ observables[position]) / 2
        step_norm = operator_norm(filtered - next_filtered)
        step_bound = defects[position] + (abs(alphas[position]) if position == 0 else 0)
        maximum_step_excess = max(maximum_step_excess, step_norm - step_bound)
        assert step_norm <= step_bound + TOLERANCE
        filtered = next_filtered
    complementary_only = np.zeros_like(reflection)
    complementary_only[np.arange(dimension), data["complements"]] = reflection[np.arange(dimension), data["complements"]]
    filter_selection_error = operator_norm(filtered - complementary_only)
    assert filter_selection_error < TOLERANCE
    eta = (n - 1 / n) * epsilon
    filter_distance = operator_norm(reflection - filtered)
    assert filter_distance <= eta + TOLERANCE
    rounded = nearest_phase_projection(data, projection)
    rounded_reflection = 2 * rounded - identity
    phase_rounding_distance = operator_norm(filtered - rounded_reflection)
    if eta < 1:
        assert phase_rounding_distance <= eta + TOLERANCE
    repair_distance = operator_norm(projection - rounded)
    repair_bound = min(1.0, eta)
    assert repair_distance <= repair_bound + TOLERANCE
    return {
        "kind": "central_operator_repair",
        "label": label,
        "n": n,
        "ambient_dimension": dimension,
        "rank": rank,
        "epsilon": epsilon,
        "largest_absolute_alpha": max(abs(alpha) for alpha in alphas),
        "alpha_bound": alpha_bound,
        "maximum_cs_spectral_error": maximum_cs_spectral_error,
        "maximum_operator_identity_error": maximum_operator_identity_error,
        "maximum_filter_step_excess": max(0.0, maximum_step_excess),
        "filter_selection_error": filter_selection_error,
        "filter_distance": filter_distance,
        "eta": eta,
        "phase_rounding_distance": phase_rounding_distance,
        "repair_distance": repair_distance,
        "repair_bound": repair_bound,
        "repair_bound_is_nontrivial": eta < 1,
    }


def run_checks() -> dict:
    rng = np.random.default_rng(SEED)
    checks = [check_tail_construction(m) for m in (3, 7, 11)]
    for n in (4, 6):
        data = support_data(n, [word for word in range(1 << n) if word.bit_count() == n // 2])
        for repetition in range(4):
            gaussian = rng.normal(size=(data["dimension"], data["rank"]))
            gaussian = gaussian + 1j * rng.normal(size=gaussian.shape)
            encoding, _ = np.linalg.qr(gaussian, mode="reduced")
            checks.append(check_operator_repair(n, encoding @ encoding.conj().T, f"random_{repetition}"))
        exact_encoding = phase_code(data, rng.uniform(-np.pi, np.pi, data["rank"]))
        gaussian = rng.normal(size=(data["dimension"], data["dimension"]))
        gaussian = gaussian + 1j * rng.normal(size=gaussian.shape)
        generator = (gaussian + gaussian.conj().T) / 2
        generator /= operator_norm(generator)
        values, vectors = np.linalg.eigh(generator)
        for angle in (0.001, 0.01, 0.05):
            unitary = (vectors * np.exp(1j * angle * values)) @ vectors.conj().T
            encoding = unitary @ exact_encoding
            checks.append(check_operator_repair(n, encoding @ encoding.conj().T, f"near_optimal_angle_{angle}"))
    operator_checks = [check for check in checks if check["kind"] == "central_operator_repair"]
    return {
        "label": "Finite supplementary checks; not a proof or novelty certificate.",
        "seed": SEED,
        "numpy_version": np.__version__,
        "tolerance": TOLERANCE,
        "all_checks_passed": True,
        "check_count": len(checks),
        "maximum_cs_spectral_error": max(check["maximum_cs_spectral_error"] for check in operator_checks),
        "maximum_operator_identity_error": max(check["maximum_operator_identity_error"] for check in operator_checks),
        "maximum_filter_step_excess": max(check["maximum_filter_step_excess"] for check in operator_checks),
        "nontrivial_operator_bounds_checked": sum(check["repair_bound_is_nontrivial"] for check in operator_checks),
        "checks": checks,
    }


def main() -> None:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("--output", type=Path, default=Path(__file__).with_name("extensions_results.json"))
    arguments = parser.parse_args()
    result = run_checks()
    arguments.output.write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8")
    print(json.dumps({key: value for key, value in result.items() if key != "checks"}, indent=2))


if __name__ == "__main__":
    main()