tests/test_edge_cases.py

"""Edge-case and robustness tests.

Tests for NaN/Inf handling, empty inputs, extreme dimensions,
and other boundary conditions that the main test suite doesn't cover.
"""

from __future__ import annotations

import math

import pytest
import torch
import torch.nn as nn

from obliteratus.analysis.whitened_svd import WhitenedSVDExtractor
from obliteratus.analysis.cross_layer import CrossLayerAlignmentAnalyzer
from obliteratus.analysis.concept_geometry import ConceptConeAnalyzer
from obliteratus.analysis.alignment_imprint import AlignmentImprintDetector
from obliteratus.analysis.multi_token_position import MultiTokenPositionAnalyzer
from obliteratus.analysis.sparse_surgery import SparseDirectionSurgeon
from obliteratus.analysis.causal_tracing import CausalRefusalTracer
from obliteratus.analysis.residual_stream import ResidualStreamDecomposer
from obliteratus.analysis.probing_classifiers import LinearRefusalProbe
from obliteratus.analysis.cross_model_transfer import TransferAnalyzer
from obliteratus.evaluation.advanced_metrics import (
    refusal_rate,
    effective_rank,
    activation_cosine_similarity,
)
from obliteratus.analysis.steering_vectors import (
    SteeringVectorFactory,
    SteeringHookManager,
    SteeringConfig,
    SteeringResult,
    compute_steering_effectiveness,
    format_steering_report,
)


# ===========================================================================
#  NaN / Inf handling
# ===========================================================================

class TestNaNInfHandling:
    """Test that modules handle degenerate inputs gracefully."""

    def test_whitened_svd_nan_activations(self):
        """WhitenedSVD with NaN — currently raises; documenting behavior."""
        harmful = [torch.tensor([float("nan"), 1.0, 2.0]) for _ in range(5)]
        harmless = [torch.randn(3) for _ in range(5)]
        extractor = WhitenedSVDExtractor()
        # NaN propagation through SVD is expected to produce NaN results
        # This documents the current behavior — ideally would guard against it
        raised = False
        result = None
        try:
            result = extractor.extract(harmful, harmless)
        except (RuntimeError, ValueError):
            raised = True
        # Either it raised an exception (acceptable) or returned a result with NaNs
        assert raised or result is not None, (
            "Should either raise on NaN input or return a result"
        )

    def test_whitened_svd_zero_activations(self):
        """WhitenedSVD with all-zero activations."""
        harmful = [torch.zeros(8) for _ in range(5)]
        harmless = [torch.zeros(8) for _ in range(5)]
        extractor = WhitenedSVDExtractor()
        result = extractor.extract(harmful, harmless)
        # Should return a valid result without crashing
        assert result is not None
        assert result.directions is not None
        assert result.singular_values is not None

    def test_concept_cone_nan_direction(self):
        """ConceptConeAnalyzer with NaN in activations — documenting behavior."""
        harmful = [torch.randn(16) for _ in range(10)]
        harmless = [torch.randn(16) for _ in range(10)]
        # Poison one activation
        harmful[3] = torch.full((16,), float("nan"))
        cat_map = {i: f"cat_{i % 3}" for i in range(10)}
        analyzer = ConceptConeAnalyzer(category_map=cat_map)
        raised = False
        result = None
        try:
            result = analyzer.analyze_layer(harmful, harmless)
        except (RuntimeError, ValueError):
            raised = True
        # Either it raised an exception (acceptable) or returned a result
        assert raised or result is not None, (
            "Should either raise on NaN input or return a result"
        )

    def test_sparse_surgery_zero_direction(self):
        """Sparse surgery with zero refusal direction."""
        W = torch.randn(32, 16)
        zero_dir = torch.zeros(16)
        surgeon = SparseDirectionSurgeon()
        result = surgeon.analyze_weight_matrix(W, zero_dir)
        assert result.mean_projection == 0.0

    def test_sparse_surgery_zero_weight(self):
        """Sparse surgery with zero weight matrix."""
        W = torch.zeros(32, 16)
        ref_dir = torch.randn(16)
        surgeon = SparseDirectionSurgeon()
        result = surgeon.analyze_weight_matrix(W, ref_dir)
        assert result.max_projection < 1e-6

    def test_effective_rank_nan_matrix(self):
        """effective_rank should handle matrix with NaN."""
        W = torch.randn(10, 10)
        W[0, 0] = float("nan")
        # Should either return a value or raise cleanly
        try:
            result = effective_rank(torch.nan_to_num(W))
            assert math.isfinite(result)
        except Exception:
            pass  # Raising is acceptable for NaN input

    def test_cosine_similarity_zero_vectors(self):
        """Cosine similarity between zero vectors."""
        a = torch.zeros(32)
        b = torch.zeros(32)
        result = activation_cosine_similarity(a, b)
        # Should be 0 or NaN, not crash
        assert math.isfinite(result) or math.isnan(result)

    def test_transfer_analyzer_nan_directions(self):
        """Transfer analyzer with NaN directions."""
        dirs_a = {0: torch.randn(16), 1: torch.tensor([float("nan")] * 16)}
        dirs_b = {0: torch.randn(16), 1: torch.randn(16)}
        analyzer = TransferAnalyzer()
        # Should not crash
        result = analyzer.analyze_cross_model(dirs_a, dirs_b)
        assert result is not None
        assert isinstance(result.mean_transfer_score, float)
        assert result.per_layer_transfer is not None


# ===========================================================================
#  Empty inputs
# ===========================================================================

class TestEmptyInputs:
    """Test graceful handling of empty or minimal inputs."""

    def test_cross_layer_empty_directions(self):
        analyzer = CrossLayerAlignmentAnalyzer()
        result = analyzer.analyze({})
        assert result.direction_persistence_score == 0.0

    def test_alignment_imprint_single_layer(self):
        """Single layer should still return a result."""
        detector = AlignmentImprintDetector()
        dirs = {0: torch.randn(32)}
        result = detector.detect_imprint(dirs)
        assert result.predicted_method in ("dpo", "rlhf", "cai", "sft", "unknown")

    def test_multi_token_single_position(self):
        """Single-position sequence."""
        ref_dir = torch.randn(16)
        acts = torch.randn(1, 16)
        analyzer = MultiTokenPositionAnalyzer()
        result = analyzer.analyze_prompt(acts, ref_dir)
        assert result.n_tokens == 1
        assert result.peak_position == 0

    def test_probing_minimal_data(self):
        """Probing with very few samples."""
        harmful = [torch.randn(8) for _ in range(3)]
        harmless = [torch.randn(8) for _ in range(3)]
        probe = LinearRefusalProbe(n_epochs=10)
        result = probe.probe_layer(harmful, harmless)
        assert 0 <= result.accuracy <= 1.0

    def test_residual_stream_single_layer(self):
        acts = {0: torch.randn(32)}
        ref_dir = torch.randn(32)
        decomposer = ResidualStreamDecomposer()
        result = decomposer.decompose(acts, ref_dir)
        assert result.n_layers == 1

    def test_causal_tracing_single_layer(self):
        acts = {0: torch.randn(32)}
        ref_dirs = {0: torch.randn(32)}
        tracer = CausalRefusalTracer()
        result = tracer.trace_from_activations(acts, ref_dirs)
        assert result.n_layers == 1

    def test_transfer_no_common_layers(self):
        """Cross-model with no overlapping layer indices."""
        dirs_a = {0: torch.randn(16), 1: torch.randn(16)}
        dirs_b = {2: torch.randn(16), 3: torch.randn(16)}
        analyzer = TransferAnalyzer()
        result = analyzer.analyze_cross_model(dirs_a, dirs_b)
        assert result.mean_transfer_score == 0.0

    def test_refusal_rate_empty_list(self):
        result = refusal_rate([])
        assert result == 0.0

    def test_refusal_rate_single_response(self):
        result = refusal_rate(["I cannot help with that."])
        assert result == 1.0


# ===========================================================================
#  Extreme dimensions
# ===========================================================================

class TestExtremeDimensions:
    """Test with unusually large or small dimensions."""

    def test_high_dimensional_directions(self):
        """Test with realistic hidden dimension (4096)."""
        hidden_dim = 4096
        torch.manual_seed(42)
        dirs = {i: torch.randn(hidden_dim) for i in range(8)}
        analyzer = TransferAnalyzer()
        result = analyzer.analyze_cross_layer(dirs)
        assert result.mean_adjacent_transfer >= 0

    def test_high_dim_sparse_surgery(self):
        """Sparse surgery with large weight matrix."""
        W = torch.randn(2048, 1024)
        ref_dir = torch.randn(1024)
        surgeon = SparseDirectionSurgeon(sparsity=0.05)
        result = surgeon.analyze_weight_matrix(W, ref_dir)
        assert result.n_rows_modified == int(0.05 * 2048)

    def test_single_dimension(self):
        """1D hidden dimension edge case."""
        dirs = {i: torch.randn(1) for i in range(4)}
        analyzer = TransferAnalyzer()
        result = analyzer.analyze_cross_layer(dirs)
        # All 1D directions are parallel or anti-parallel, so cosine is always 1.0
        assert result.mean_adjacent_transfer >= 0.99

    def test_many_layers_imprint(self):
        """Alignment imprint with many layers (128)."""
        dirs = {i: torch.randn(32) for i in range(128)}
        detector = AlignmentImprintDetector()
        result = detector.detect_imprint(dirs)
        total = (result.dpo_probability + result.rlhf_probability +
                 result.cai_probability + result.sft_probability)
        assert abs(total - 1.0) < 0.01

    @pytest.mark.parametrize("n_prompts", [1, 2, 5, 50, 100])
    def test_concept_cone_varying_prompt_counts(self, n_prompts):
        """Concept cone with varying numbers of prompts."""
        harmful = [torch.randn(16) for _ in range(n_prompts)]
        harmless = [torch.randn(16) for _ in range(n_prompts)]
        cat_map = {i: f"cat_{i % 3}" for i in range(n_prompts)}
        analyzer = ConceptConeAnalyzer(category_map=cat_map, min_category_size=1)
        result = analyzer.analyze_layer(harmful, harmless)
        # Should produce a valid result regardless of prompt count
        assert result is not None
        assert result.general_direction is not None
        assert result.category_count >= 1

    @pytest.mark.parametrize("seq_len", [1, 2, 10, 100, 500])
    def test_multi_token_varying_lengths(self, seq_len):
        """Multi-token analysis with varying sequence lengths."""
        ref_dir = torch.randn(16)
        acts = torch.randn(seq_len, 16)
        analyzer = MultiTokenPositionAnalyzer()
        result = analyzer.analyze_prompt(acts, ref_dir)
        assert result.n_tokens == seq_len

    @pytest.mark.parametrize("hidden_dim", [2, 4, 16, 64, 256, 1024])
    def test_probing_varying_dimensions(self, hidden_dim):
        """Probing classifier across different hidden dims (min 2 for SGD)."""
        harmful = [torch.randn(hidden_dim) * 2 + 1 for _ in range(10)]
        harmless = [torch.randn(hidden_dim) * 2 - 1 for _ in range(10)]
        probe = LinearRefusalProbe(n_epochs=20)
        result = probe.probe_layer(harmful, harmless)
        assert 0 <= result.accuracy <= 1.0


# ===========================================================================
#  Refusal rate edge cases
# ===========================================================================

class TestRefusalRateEdgeCases:
    """Edge cases for refusal rate detection."""

    def test_novel_refusal_phrasing(self):
        """Test that unusual refusal phrasings might be missed (known limitation)."""
        novel_refusals = [
            "That request falls outside my operational parameters.",
            "I'm designed to be helpful but this conflicts with my guidelines.",
        ]
        # These should ideally be detected but string matching may miss them
        rate = refusal_rate(novel_refusals)
        # This tests the known limitation — rate may be 0 (string matching misses novel phrasings)
        assert 0 <= rate <= 1.0

    def test_false_positive_sorry(self):
        """'I'm sorry to hear that' followed by helpful content."""
        responses = [
            "I'm sorry to hear that. Here's how you can solve the problem: first...",
        ]
        rate = refusal_rate(responses, mode="prefix")
        # Known limitation: prefix matching produces false positives here
        # Just verify it returns a valid float
        assert 0 <= rate <= 1.0

    def test_case_insensitivity(self):
        responses = ["I CANNOT ASSIST WITH THAT REQUEST."]
        rate = refusal_rate(responses, mode="substring")
        assert rate == 1.0

    def test_whitespace_handling(self):
        responses = ["  I'm sorry, but I can't help with that.  "]
        rate = refusal_rate(responses, mode="prefix")
        # Should handle leading whitespace
        assert 0 <= rate <= 1.0


# ===========================================================================
#  Steering Vectors
# ===========================================================================

class TestSteeringVectors:
    """Tests for the steering vector inference-time intervention system."""

    def test_from_refusal_direction(self):
        d = torch.randn(32)
        vec = SteeringVectorFactory.from_refusal_direction(d, source_layer=5)
        assert vec.label == "refusal"
        assert vec.source_layer == 5
        assert vec.default_alpha == -1.0
        assert abs(vec.direction.norm().item() - 1.0) < 0.01

    def test_from_contrastive_pairs(self):
        pos = [torch.randn(16) + 2 for _ in range(10)]
        neg = [torch.randn(16) - 2 for _ in range(10)]
        vec = SteeringVectorFactory.from_contrastive_pairs(pos, neg, label="test")
        assert vec.label == "test"
        assert abs(vec.direction.norm().item() - 1.0) < 0.01
        assert "n_positive" in vec.metadata

    def test_combine_vectors(self):
        v1 = SteeringVectorFactory.from_refusal_direction(torch.randn(32))
        v2 = SteeringVectorFactory.from_refusal_direction(torch.randn(32))
        combined = SteeringVectorFactory.combine([v1, v2], label="merged")
        assert combined.label == "merged"
        assert abs(combined.direction.norm().item() - 1.0) < 0.01

    def test_combine_single(self):
        v = SteeringVectorFactory.from_refusal_direction(torch.randn(16))
        combined = SteeringVectorFactory.combine([v])
        assert abs(combined.direction.norm().item() - 1.0) < 0.01

    def test_combine_empty_raises(self):
        with pytest.raises(ValueError):
            SteeringVectorFactory.combine([])

    def test_hook_manager_lifecycle(self):
        """Test install/remove lifecycle without a real model."""
        manager = SteeringHookManager()
        assert not manager.is_active
        manager.remove()  # Should not crash even with no hooks
        assert not manager.is_active

    def test_hook_with_simple_model(self):
        """Test steering on a simple nn.Sequential model."""
        model = nn.Sequential(
            nn.Linear(16, 16),
            nn.ReLU(),
            nn.Linear(16, 16),
            nn.ReLU(),
            nn.Linear(16, 8),
        )

        vec = SteeringVectorFactory.from_refusal_direction(torch.randn(16))
        config = SteeringConfig(
            vectors=[vec],
            target_layers=[0, 2],  # steer at first and third linear layers
            alpha=1.0,
        )

        manager = SteeringHookManager()
        # Install on specific modules
        layers = list(model.children())
        result = manager.install(model, config, layer_modules=layers)
        assert result.hooks_installed == 2
        assert manager.is_active

        # Run a forward pass (should not crash)
        x = torch.randn(1, 16)
        output = model(x)
        assert output.shape == (1, 8)

        # Remove hooks
        manager.remove()
        assert not manager.is_active

    def test_steering_effectiveness_remove(self):
        eff = compute_steering_effectiveness(2.0, 0.5, direction="remove")
        assert 0 < eff < 1.0  # Reduced but not eliminated

    def test_steering_effectiveness_perfect_remove(self):
        eff = compute_steering_effectiveness(2.0, 0.0, direction="remove")
        assert eff == 1.0

    def test_steering_effectiveness_no_change(self):
        eff = compute_steering_effectiveness(2.0, 2.0, direction="remove")
        assert eff == 0.0

    def test_steering_effectiveness_add(self):
        eff = compute_steering_effectiveness(1.0, 3.0, direction="add")
        assert eff == 1.0  # Capped at 1.0

    def test_format_report(self):
        vec = SteeringVectorFactory.from_refusal_direction(torch.randn(32))
        config = SteeringConfig(vectors=[vec], target_layers=[3, 5], alpha=0.5)
        result = SteeringResult(config=config, hooks_installed=2, total_steered_layers=2)
        report = format_steering_report(result)
        assert "Steering" in report
        assert "refusal" in report

    def test_steering_config_position_modes(self):
        """Test different position modes in config."""
        for pos in ["all", "last", "first"]:
            config = SteeringConfig(
                vectors=[SteeringVectorFactory.from_refusal_direction(torch.randn(8))],
                target_layers=[0],
                position=pos,
            )
            assert config.position == pos

    def test_imports(self):
        from obliteratus.analysis import SteeringVectorFactory, SteeringHookManager
        assert SteeringVectorFactory is not None
        assert SteeringHookManager is not None


class TestParametrizedDimensions:
    """Parametrized tests across different hidden dimensions."""

    @pytest.mark.parametrize("hidden_dim", [2, 8, 64, 256, 768])
    def test_whitened_svd_various_dims(self, hidden_dim):
        n_samples = max(4, hidden_dim // 4)
        harmful = [torch.randn(hidden_dim) for _ in range(n_samples)]
        harmless = [torch.randn(hidden_dim) for _ in range(n_samples)]
        extractor = WhitenedSVDExtractor()
        result = extractor.extract(harmful, harmless, n_directions=1)
        assert result.directions.shape[1] == hidden_dim

    @pytest.mark.parametrize("hidden_dim", [2, 8, 64, 256])
    def test_cross_layer_various_dims(self, hidden_dim):
        directions = {i: torch.randn(hidden_dim) for i in range(4)}
        analyzer = CrossLayerAlignmentAnalyzer()
        result = analyzer.analyze(directions)
        assert 0.0 <= result.direction_persistence_score <= 1.0

    @pytest.mark.parametrize("hidden_dim", [4, 32, 128])
    def test_sparse_surgery_various_dims(self, hidden_dim):
        weight = torch.randn(hidden_dim, hidden_dim)
        direction = torch.randn(hidden_dim)
        direction = direction / direction.norm()
        surgeon = SparseDirectionSurgeon()
        result = surgeon.analyze_weight_matrix(weight, direction, layer_idx=0)
        assert 0.0 <= result.energy_removed <= 1.0

    @pytest.mark.parametrize("n_layers", [1, 4, 12, 32])
    def test_imprint_various_layer_counts(self, n_layers):
        directions = {i: torch.randn(64) for i in range(n_layers)}
        detector = AlignmentImprintDetector()
        result = detector.detect_imprint(directions)
        assert result.predicted_method in ("dpo", "rlhf", "cai", "sft", "unknown")


class TestExceptionPaths:
    """Tests for error handling and boundary conditions."""

    def test_whitened_svd_mismatched_dims(self):
        """Harmful and harmless with different hidden dims should fail or handle gracefully."""
        harmful = [torch.randn(64) for _ in range(10)]
        harmless = [torch.randn(32) for _ in range(10)]
        extractor = WhitenedSVDExtractor()
        with pytest.raises(Exception):
            extractor.extract(harmful, harmless, n_directions=1)

    def test_whitened_svd_single_sample(self):
        """Single sample should not crash (may return 0 directions due to insufficient data)."""
        harmful = [torch.randn(32)]
        harmless = [torch.randn(32)]
        extractor = WhitenedSVDExtractor()
        result = extractor.extract(harmful, harmless, n_directions=1)
        assert result.directions.shape[1] == 32  # hidden dim preserved

    def test_sparse_surgery_zero_direction(self):
        """Zero direction vector should not crash."""
        weight = torch.randn(16, 16)
        direction = torch.zeros(16)
        surgeon = SparseDirectionSurgeon()
        # Should handle gracefully (possibly returning 0 energy)
        result = surgeon.analyze_weight_matrix(weight, direction, layer_idx=0)
        assert result is not None

    def test_cross_layer_single_layer(self):
        """Single layer directions should still produce a result."""
        directions = {0: torch.randn(32)}
        analyzer = CrossLayerAlignmentAnalyzer()
        result = analyzer.analyze(directions)
        assert result is not None