"""
CRITICAL: This test must pass before any other file is extended.
It verifies the core algebraic invariant of the entire system.
"""

import numpy as np
import pytest
from hypothesis import given, settings
from hypothesis import strategies as st

from algebra.rotor import word_transition_rotor
from algebra.versor import (
    unitize_versor,
    versor_apply,
    versor_condition,
    versor_unit_residual,
)


def _positive_unit_reflector(seed=None) -> np.ndarray:
    """
    Construct a true positive-norm Cl(4,1) grade-1 versor.

    The current field action uses V * F * reverse(V), so the operator fixture
    must satisfy V * reverse(V) = +1, not -1. We therefore keep the fifth
    basis component bounded below the positive four-space norm.
    """
    rng = np.random.default_rng(seed)
    vec4 = rng.standard_normal(4).astype(np.float32)
    norm4 = float(np.linalg.norm(vec4))
    if norm4 < 1e-6:
        vec4[0] = 1.0
        norm4 = 1.0

    vec = np.zeros(5, dtype=np.float32)
    vec[:4] = vec4
    vec[4] = 0.25 * norm4 * np.tanh(float(rng.standard_normal()))

    mv = np.zeros(32, dtype=np.float32)
    mv[1:6] = vec
    return unitize_versor(mv)


@given(st.integers(min_value=0, max_value=99))
@settings(max_examples=100)
def test_versor_apply_preserves_manifold(seed):
    V = _positive_unit_reflector(seed)
    F = _positive_unit_reflector(seed + 1000)
    result = versor_apply(V, F)
    cond = versor_condition(result)
    assert cond < 1e-4, f"versor_apply broke the manifold: condition={cond:.2e}"


def test_unitize_clean_scalar_constructs_positive_unit_versor():
    raw = np.zeros(32, dtype=np.float32)
    raw[0] = 2.0
    V = unitize_versor(raw)
    assert np.allclose(V[0], 1.0, atol=1e-7)
    assert versor_condition(V) < 1e-7


def test_unitize_rejects_non_scalar_residue_instead_of_hash_fallback():
    dirty = np.zeros(32, dtype=np.float32)
    dirty[0] = np.sqrt(0.5)
    dirty[1] = np.sqrt(0.5)

    with pytest.raises(ValueError, match="bad_residue"):
        unitize_versor(dirty)


def test_unitize_rejects_non_positive_scalar_norm():
    negative_norm = np.zeros(32, dtype=np.float32)
    negative_norm[5] = 1.0

    with pytest.raises(ValueError, match="bad_scalar"):
        unitize_versor(negative_norm)


def test_versor_unit_residual_can_accept_signed_manifold_versors():
    negative_norm = np.zeros(32, dtype=np.float32)
    negative_norm[5] = 1.0

    assert versor_condition(negative_norm) > 1.0
    assert versor_unit_residual(negative_norm, allow_negative=True) < 1e-7


def test_word_transition_rotor_handles_antipodal_scalar_inputs_as_closed_transition():
    A = np.zeros(32, dtype=np.float32)
    A[0] = 1.0
    B = np.zeros(32, dtype=np.float32)
    B[0] = -1.0

    R = word_transition_rotor(A, B)

    assert np.allclose(R[0], -1.0, atol=1e-7)
    assert versor_condition(R) < 1e-6


def test_composition_closed():
    V1 = _positive_unit_reflector(0)
    V2 = _positive_unit_reflector(1)
    F = _positive_unit_reflector(2)
    F2 = versor_apply(V1, F)
    F3 = versor_apply(V2, F2)
    assert versor_condition(F3) < 1e-4


def test_versor_apply_preserves_null_property_for_null_inputs():
    """Null vectors (CGA points) map to null vectors under versor sandwich.

    Updated 2026-05-17: `versor_apply` now routes null inputs around
    the unit-versor closure boundary so the null property is preserved.
    The previous test name claimed runtime closure was required for
    null inputs; that contradicted the CGA geometric semantics
    (Euclidean points stay points under conformal transformations)
    and the un-skipped null-preservation invariant in
    `tests/test_architectural_invariants.py::TestINV06NullConePreservation`.

    Non-null field states still pass through closure unchanged — see
    `test_composition_closed` and `test_identity_versor` for those.
    """
    from algebra.cga import cga_inner
    identity = np.zeros(32, dtype=np.float32)
    identity[0] = 1.0
    null_like = np.zeros(32, dtype=np.float32)
    null_like[1] = 1.0
    null_like[5] = 1.0

    # Sanity: the constructed input is null under the CGA metric
    # (e1·e1=+1, e_-·e_-=-1, cross terms cancel → self-inner = 0).
    assert abs(float(cga_inner(null_like, null_like))) < 1e-9

    result = versor_apply(identity, null_like)

    # The result must remain null, not closed to the unit-versor shell.
    assert abs(float(cga_inner(result, result))) < 1e-5


def test_identity_versor():
    identity = np.zeros(32, dtype=np.float32)
    identity[0] = 1.0
    F = _positive_unit_reflector(42)
    result = versor_apply(identity, F)
    assert np.allclose(result, F, atol=1e-5)