| """Tests for the opt-in telemetry module.""" |
|
|
| import json |
| import os |
| import tempfile |
| from dataclasses import dataclass, field |
| from pathlib import Path |
| from unittest.mock import MagicMock, patch |
|
|
| import torch |
|
|
| from obliteratus.telemetry import ( |
| _ALLOWED_METHOD_CONFIG_KEYS, |
| _direction_stats, |
| _extract_excise_details, |
| _extract_prompt_counts, |
| _extract_analysis_insights, |
| _is_mount_point, |
| _test_writable, |
| build_report, |
| disable_telemetry, |
| enable_telemetry, |
| is_enabled, |
| maybe_send_informed_report, |
| maybe_send_pipeline_report, |
| restore_from_hub, |
| send_report, |
| storage_diagnostic, |
| ) |
|
|
|
|
| def _reset_telemetry(): |
| import obliteratus.telemetry as t |
| t._enabled = None |
|
|
|
|
| |
|
|
|
|
| class TestTelemetryConfig: |
| """Test telemetry enable/disable logic.""" |
|
|
| def setup_method(self): |
| _reset_telemetry() |
|
|
| def test_disabled_by_default(self): |
| with patch.dict(os.environ, {}, clear=True): |
| _reset_telemetry() |
| assert not is_enabled() |
|
|
| def test_enabled_by_default_on_hf_spaces(self): |
| with patch.dict(os.environ, {"SPACE_ID": "user/space"}, clear=True): |
| import obliteratus.telemetry as t |
| old_val = t._ON_HF_SPACES |
| t._ON_HF_SPACES = True |
| _reset_telemetry() |
| assert is_enabled() |
| t._ON_HF_SPACES = old_val |
|
|
| def test_disable_via_env_zero(self): |
| with patch.dict(os.environ, {"OBLITERATUS_TELEMETRY": "0"}): |
| _reset_telemetry() |
| assert not is_enabled() |
|
|
| def test_disable_via_env_false(self): |
| with patch.dict(os.environ, {"OBLITERATUS_TELEMETRY": "false"}): |
| _reset_telemetry() |
| assert not is_enabled() |
|
|
| def test_enable_via_env_explicit(self): |
| with patch.dict(os.environ, {"OBLITERATUS_TELEMETRY": "1"}): |
| _reset_telemetry() |
| assert is_enabled() |
|
|
| def test_enable_programmatically(self): |
| enable_telemetry() |
| assert is_enabled() |
|
|
| def test_disable_programmatically(self): |
| enable_telemetry() |
| assert is_enabled() |
| disable_telemetry() |
| assert not is_enabled() |
|
|
| def test_programmatic_overrides_env(self): |
| with patch.dict(os.environ, {"OBLITERATUS_TELEMETRY": "1"}): |
| disable_telemetry() |
| assert not is_enabled() |
|
|
|
|
| |
|
|
|
|
| class TestBuildReport: |
| """Test report payload construction.""" |
|
|
| def _base_kwargs(self, **overrides): |
| defaults = dict( |
| architecture="LlamaForCausalLM", |
| num_layers=32, |
| num_heads=32, |
| hidden_size=4096, |
| total_params=8_000_000_000, |
| method="advanced", |
| method_config={"n_directions": 4, "norm_preserve": True}, |
| quality_metrics={"perplexity": 5.2, "refusal_rate": 0.05}, |
| ) |
| defaults.update(overrides) |
| return defaults |
|
|
| def test_schema_version_2(self): |
| report = build_report(**self._base_kwargs()) |
| assert report["schema_version"] == 2 |
|
|
| def test_basic_fields(self): |
| report = build_report(**self._base_kwargs()) |
| assert report["model"]["architecture"] == "LlamaForCausalLM" |
| assert report["model"]["num_layers"] == 32 |
| assert report["model"]["total_params"] == 8_000_000_000 |
| assert report["method"] == "advanced" |
| assert report["quality_metrics"]["refusal_rate"] == 0.05 |
| assert len(report["session_id"]) == 32 |
|
|
| def test_filters_unknown_config_keys(self): |
| report = build_report(**self._base_kwargs( |
| method_config={"n_directions": 1, "secret_flag": True, "nuke": "boom"}, |
| )) |
| assert "n_directions" in report["method_config"] |
| assert "secret_flag" not in report["method_config"] |
| assert "nuke" not in report["method_config"] |
|
|
| def test_allows_all_valid_config_keys(self): |
| """Every key in the allowlist should pass through.""" |
| config = {k: True for k in _ALLOWED_METHOD_CONFIG_KEYS} |
| report = build_report(**self._base_kwargs(method_config=config)) |
| for k in _ALLOWED_METHOD_CONFIG_KEYS: |
| assert k in report["method_config"], f"Missing allowlisted key: {k}" |
|
|
| def test_no_model_name_in_report(self): |
| report = build_report(**self._base_kwargs()) |
| report_str = json.dumps(report) |
| assert "meta-llama" not in report_str |
| assert "Llama-3" not in report_str |
|
|
| def test_environment_info(self): |
| report = build_report(**self._base_kwargs()) |
| env = report["environment"] |
| assert "python_version" in env |
| assert "os" in env |
| assert "arch" in env |
|
|
| def test_stage_durations(self): |
| durations = {"summon": 2.5, "probe": 10.1, "distill": 3.2} |
| report = build_report(**self._base_kwargs(stage_durations=durations)) |
| assert report["stage_durations"] == durations |
|
|
| def test_direction_stats(self): |
| stats = {"direction_norms": {"10": 0.95}, "mean_direction_persistence": 0.87} |
| report = build_report(**self._base_kwargs(direction_stats=stats)) |
| assert report["direction_stats"]["mean_direction_persistence"] == 0.87 |
|
|
| def test_excise_details(self): |
| details = {"modified_count": 128, "used_techniques": ["head_surgery"]} |
| report = build_report(**self._base_kwargs(excise_details=details)) |
| assert report["excise_details"]["modified_count"] == 128 |
|
|
| def test_prompt_counts(self): |
| counts = {"harmful": 33, "harmless": 33, "jailbreak": 15} |
| report = build_report(**self._base_kwargs(prompt_counts=counts)) |
| assert report["prompt_counts"]["harmful"] == 33 |
| assert report["prompt_counts"]["jailbreak"] == 15 |
|
|
| def test_gpu_memory(self): |
| mem = {"peak_allocated_gb": 7.2, "peak_reserved_gb": 8.0} |
| report = build_report(**self._base_kwargs(gpu_memory=mem)) |
| assert report["gpu_memory"]["peak_allocated_gb"] == 7.2 |
|
|
| def test_analysis_insights_filtered(self): |
| """Only allowlisted analysis keys should pass through.""" |
| insights = { |
| "detected_alignment_method": "DPO", |
| "alignment_confidence": 0.92, |
| "secret_internal_data": "should not appear", |
| } |
| report = build_report(**self._base_kwargs(analysis_insights=insights)) |
| assert report["analysis_insights"]["detected_alignment_method"] == "DPO" |
| assert "secret_internal_data" not in report["analysis_insights"] |
|
|
| def test_informed_extras(self): |
| extras = {"ouroboros_passes": 3, "final_refusal_rate": 0.02, "total_duration": 120.5} |
| report = build_report(**self._base_kwargs(informed_extras=extras)) |
| assert report["informed"]["ouroboros_passes"] == 3 |
|
|
| def test_optional_fields_omitted_when_empty(self): |
| """Optional fields should not appear when not provided.""" |
| report = build_report(**self._base_kwargs()) |
| assert "stage_durations" not in report |
| assert "direction_stats" not in report |
| assert "excise_details" not in report |
| assert "prompt_counts" not in report |
| assert "gpu_memory" not in report |
| assert "analysis_insights" not in report |
| assert "informed" not in report |
|
|
|
|
| |
|
|
|
|
| class TestDirectionStats: |
| """Test direction quality metric extraction.""" |
|
|
| def test_direction_norms(self): |
| pipeline = MagicMock() |
| pipeline.refusal_directions = { |
| 0: torch.randn(128), |
| 1: torch.randn(128), |
| } |
| pipeline.refusal_subspaces = {} |
| stats = _direction_stats(pipeline) |
| assert "direction_norms" in stats |
| assert "0" in stats["direction_norms"] |
| assert "1" in stats["direction_norms"] |
|
|
| def test_direction_persistence(self): |
| """Adjacent layers with similar directions should have high persistence.""" |
| d = torch.randn(128) |
| d = d / d.norm() |
| pipeline = MagicMock() |
| pipeline.refusal_directions = {0: d, 1: d + 0.01 * torch.randn(128)} |
| pipeline.refusal_subspaces = {} |
| stats = _direction_stats(pipeline) |
| assert "mean_direction_persistence" in stats |
| assert stats["mean_direction_persistence"] > 0.9 |
|
|
| def test_effective_rank(self): |
| """Multi-direction subspace should yield effective rank > 1.""" |
| pipeline = MagicMock() |
| pipeline.refusal_directions = {0: torch.randn(128)} |
| |
| sub = torch.randn(4, 128) |
| pipeline.refusal_subspaces = {0: sub} |
| stats = _direction_stats(pipeline) |
| assert "effective_ranks" in stats |
| assert float(stats["effective_ranks"]["0"]) > 1.0 |
|
|
| def test_empty_directions(self): |
| pipeline = MagicMock() |
| pipeline.refusal_directions = {} |
| pipeline.refusal_subspaces = {} |
| stats = _direction_stats(pipeline) |
| assert stats == {} |
|
|
|
|
| |
|
|
|
|
| class TestExciseDetails: |
| def test_basic_excise_details(self): |
| pipeline = MagicMock() |
| pipeline._excise_modified_count = 64 |
| pipeline._refusal_heads = {10: [(0, 0.9), (3, 0.8)], 11: [(1, 0.7)]} |
| pipeline._sae_directions = {} |
| pipeline._expert_safety_scores = {} |
| pipeline._layer_excise_weights = {} |
| pipeline._expert_directions = {} |
| pipeline._steering_hooks = [] |
| pipeline.invert_refusal = False |
| pipeline.project_embeddings = False |
| pipeline.activation_steering = False |
| pipeline.expert_transplant = False |
|
|
| details = _extract_excise_details(pipeline) |
| assert details["modified_count"] == 64 |
| assert details["head_surgery_layers"] == 2 |
| assert details["total_heads_projected"] == 3 |
| assert "head_surgery" in details["used_techniques"] |
|
|
| def test_adaptive_weights(self): |
| pipeline = MagicMock() |
| pipeline._excise_modified_count = None |
| pipeline._refusal_heads = {} |
| pipeline._sae_directions = {} |
| pipeline._expert_safety_scores = {} |
| pipeline._layer_excise_weights = {0: 0.2, 1: 0.8, 2: 0.5} |
| pipeline._expert_directions = {} |
| pipeline._steering_hooks = [] |
| pipeline.invert_refusal = False |
| pipeline.project_embeddings = False |
| pipeline.activation_steering = False |
| pipeline.expert_transplant = False |
|
|
| details = _extract_excise_details(pipeline) |
| assert details["adaptive_weight_min"] == 0.2 |
| assert details["adaptive_weight_max"] == 0.8 |
| assert "layer_adaptive" in details["used_techniques"] |
|
|
|
|
| |
|
|
|
|
| class TestPromptCounts: |
| def test_basic_counts(self): |
| pipeline = MagicMock() |
| pipeline.harmful_prompts = ["a"] * 33 |
| pipeline.harmless_prompts = ["b"] * 33 |
| pipeline.jailbreak_prompts = None |
| counts = _extract_prompt_counts(pipeline) |
| assert counts["harmful"] == 33 |
| assert counts["harmless"] == 33 |
| assert "jailbreak" not in counts |
|
|
| def test_with_jailbreak(self): |
| pipeline = MagicMock() |
| pipeline.harmful_prompts = ["a"] * 33 |
| pipeline.harmless_prompts = ["b"] * 33 |
| pipeline.jailbreak_prompts = ["c"] * 10 |
| counts = _extract_prompt_counts(pipeline) |
| assert counts["jailbreak"] == 10 |
|
|
|
|
| |
|
|
|
|
| class TestSendReport: |
| def setup_method(self): |
| _reset_telemetry() |
|
|
| def test_does_not_send_when_disabled(self): |
| disable_telemetry() |
| with patch("obliteratus.telemetry._send_sync") as mock_send: |
| send_report({"test": True}) |
| mock_send.assert_not_called() |
|
|
| def test_sends_when_enabled(self): |
| enable_telemetry() |
| with patch("obliteratus.telemetry._send_sync") as mock_send: |
| send_report({"test": True}) |
| import time |
| time.sleep(0.1) |
| mock_send.assert_called_once_with({"test": True}) |
|
|
| def test_send_failure_is_silent(self): |
| enable_telemetry() |
| with patch("obliteratus.telemetry._send_sync", side_effect=Exception("network down")) as mock_send: |
| |
| send_report({"test": True}) |
| import time |
| time.sleep(0.1) |
| mock_send.assert_called_once_with({"test": True}) |
|
|
|
|
| |
|
|
|
|
| def _make_mock_pipeline(): |
| """Build a mock pipeline with all fields the telemetry module reads.""" |
| p = MagicMock() |
| p.handle.summary.return_value = { |
| "architecture": "LlamaForCausalLM", |
| "num_layers": 32, |
| "num_heads": 32, |
| "hidden_size": 4096, |
| "total_params": 8_000_000_000, |
| } |
| p.method = "advanced" |
| p.n_directions = 4 |
| p.norm_preserve = True |
| p.regularization = 0.1 |
| p.refinement_passes = 2 |
| p.project_biases = True |
| p.use_chat_template = True |
| p.use_whitened_svd = True |
| p.true_iterative_refinement = False |
| p.use_jailbreak_contrast = False |
| p.layer_adaptive_strength = False |
| p.attention_head_surgery = True |
| p.safety_neuron_masking = False |
| p.per_expert_directions = False |
| p.use_sae_features = False |
| p.invert_refusal = False |
| p.project_embeddings = False |
| p.embed_regularization = 0.5 |
| p.activation_steering = False |
| p.steering_strength = 0.3 |
| p.expert_transplant = False |
| p.transplant_blend = 0.3 |
| p.reflection_strength = 2.0 |
| p.quantization = None |
|
|
| p._quality_metrics = {"perplexity": 5.2, "coherence": 0.8, "refusal_rate": 0.05} |
| p._strong_layers = [10, 11, 12, 13] |
| p._stage_durations = {"summon": 3.0, "probe": 12.5, "distill": 4.1, "excise": 2.0, "verify": 8.3, "rebirth": 5.0} |
| p._excise_modified_count = 128 |
|
|
| |
| d = torch.randn(4096) |
| d = d / d.norm() |
| p.refusal_directions = {10: d, 11: d + 0.01 * torch.randn(4096), 12: d, 13: d} |
| p.refusal_subspaces = {10: torch.randn(4, 4096)} |
|
|
| |
| p._refusal_heads = {10: [(0, 0.9), (3, 0.8)]} |
| p._sae_directions = {} |
| p._expert_safety_scores = {} |
| p._layer_excise_weights = {} |
| p._expert_directions = {} |
| p._steering_hooks = [] |
|
|
| |
| p.harmful_prompts = ["x"] * 33 |
| p.harmless_prompts = ["y"] * 33 |
| p.jailbreak_prompts = None |
|
|
| return p |
|
|
|
|
| class TestPipelineIntegration: |
| def setup_method(self): |
| _reset_telemetry() |
|
|
| def test_does_nothing_when_disabled(self): |
| disable_telemetry() |
| with patch("obliteratus.telemetry.send_report") as mock_send: |
| maybe_send_pipeline_report(_make_mock_pipeline()) |
| mock_send.assert_not_called() |
|
|
| def test_comprehensive_report(self): |
| """Verify that all data points are extracted from the pipeline.""" |
| enable_telemetry() |
| p = _make_mock_pipeline() |
| with patch("obliteratus.telemetry.send_report") as mock_send: |
| maybe_send_pipeline_report(p) |
| mock_send.assert_called_once() |
| report = mock_send.call_args[0][0] |
|
|
| |
| assert report["schema_version"] == 2 |
| assert report["model"]["architecture"] == "LlamaForCausalLM" |
| assert report["method"] == "advanced" |
|
|
| |
| cfg = report["method_config"] |
| assert cfg["n_directions"] == 4 |
| assert cfg["norm_preserve"] is True |
| assert cfg["use_whitened_svd"] is True |
| assert cfg["attention_head_surgery"] is True |
|
|
| |
| assert report["quality_metrics"]["perplexity"] == 5.2 |
| assert report["quality_metrics"]["refusal_rate"] == 0.05 |
|
|
| |
| assert "stage_durations" in report |
| assert report["stage_durations"]["summon"] == 3.0 |
| assert report["stage_durations"]["verify"] == 8.3 |
|
|
| |
| assert report["strong_layers"] == [10, 11, 12, 13] |
|
|
| |
| assert "direction_stats" in report |
| assert "direction_norms" in report["direction_stats"] |
| assert "mean_direction_persistence" in report["direction_stats"] |
|
|
| |
| assert "excise_details" in report |
| assert report["excise_details"]["modified_count"] == 128 |
| assert "head_surgery" in report["excise_details"]["used_techniques"] |
|
|
| |
| assert report["prompt_counts"]["harmful"] == 33 |
| assert report["prompt_counts"]["harmless"] == 33 |
|
|
| |
| assert "os" in report["environment"] |
| assert "python_version" in report["environment"] |
|
|
|
|
| |
|
|
|
|
| @dataclass |
| class _MockInsights: |
| detected_alignment_method: str = "DPO" |
| alignment_confidence: float = 0.92 |
| alignment_probabilities: dict = field(default_factory=lambda: {"DPO": 0.92, "RLHF": 0.05}) |
| cone_is_polyhedral: bool = True |
| cone_dimensionality: float = 3.2 |
| mean_pairwise_cosine: float = 0.45 |
| direction_specificity: dict = field(default_factory=lambda: {"violence": 0.8}) |
| cluster_count: int = 3 |
| direction_persistence: float = 0.87 |
| mean_refusal_sparsity_index: float = 0.15 |
| recommended_sparsity: float = 0.1 |
| use_sparse_surgery: bool = True |
| estimated_robustness: str = "medium" |
| self_repair_estimate: float = 0.3 |
| entanglement_score: float = 0.2 |
| entangled_layers: list = field(default_factory=lambda: [15, 16]) |
| clean_layers: list = field(default_factory=lambda: [10, 11, 12]) |
| recommended_n_directions: int = 6 |
| recommended_regularization: float = 0.05 |
| recommended_refinement_passes: int = 3 |
| recommended_layers: list = field(default_factory=lambda: [10, 11, 12, 13]) |
| skip_layers: list = field(default_factory=lambda: [15]) |
|
|
|
|
| @dataclass |
| class _MockInformedReport: |
| insights: _MockInsights = field(default_factory=_MockInsights) |
| ouroboros_passes: int = 2 |
| final_refusal_rate: float = 0.02 |
| analysis_duration: float = 15.3 |
| total_duration: float = 85.7 |
|
|
|
|
| class TestInformedPipelineIntegration: |
| def setup_method(self): |
| _reset_telemetry() |
|
|
| def test_does_nothing_when_disabled(self): |
| disable_telemetry() |
| with patch("obliteratus.telemetry.send_report") as mock_send: |
| maybe_send_informed_report(_make_mock_pipeline(), _MockInformedReport()) |
| mock_send.assert_not_called() |
|
|
| def test_comprehensive_informed_report(self): |
| enable_telemetry() |
| p = _make_mock_pipeline() |
| report_obj = _MockInformedReport() |
|
|
| with patch("obliteratus.telemetry.send_report") as mock_send: |
| maybe_send_informed_report(p, report_obj) |
| mock_send.assert_called_once() |
| report = mock_send.call_args[0][0] |
|
|
| |
| assert report["schema_version"] == 2 |
| assert report["model"]["architecture"] == "LlamaForCausalLM" |
| assert "direction_stats" in report |
| assert "excise_details" in report |
|
|
| |
| ai = report["analysis_insights"] |
| assert ai["detected_alignment_method"] == "DPO" |
| assert ai["alignment_confidence"] == 0.92 |
| assert ai["cone_is_polyhedral"] is True |
| assert ai["cone_dimensionality"] == 3.2 |
| assert ai["cluster_count"] == 3 |
| assert ai["self_repair_estimate"] == 0.3 |
| assert ai["entanglement_score"] == 0.2 |
| assert ai["recommended_n_directions"] == 6 |
|
|
| |
| inf = report["informed"] |
| assert inf["ouroboros_passes"] == 2 |
| assert inf["final_refusal_rate"] == 0.02 |
| assert inf["analysis_duration"] == 15.3 |
| assert inf["total_duration"] == 85.7 |
|
|
| def test_analysis_insights_filter_unknown_keys(self): |
| enable_telemetry() |
| _make_mock_pipeline() |
|
|
| @dataclass |
| class _BadInsights(_MockInsights): |
| secret_sauce: str = "should not appear" |
|
|
| report_obj = _MockInformedReport(insights=_BadInsights()) |
| insights = _extract_analysis_insights(report_obj) |
| assert "detected_alignment_method" in insights |
| assert "secret_sauce" not in insights |
|
|
|
|
| |
|
|
|
|
| class TestStageDurationTracking: |
| def test_emit_records_durations(self): |
| """Verify _emit stores durations in _stage_durations dict.""" |
| from obliteratus.abliterate import AbliterationPipeline |
|
|
| p = AbliterationPipeline.__new__(AbliterationPipeline) |
| p._stage_durations = {} |
| p._excise_modified_count = None |
| p._on_stage = lambda r: None |
|
|
| p._emit("summon", "done", "loaded", duration=3.5) |
| p._emit("probe", "done", "probed", duration=10.2) |
| p._emit("excise", "done", "excised", duration=2.1, modified_count=64) |
|
|
| assert p._stage_durations == {"summon": 3.5, "probe": 10.2, "excise": 2.1} |
| assert p._excise_modified_count == 64 |
|
|
| def test_running_status_does_not_record(self): |
| """Only 'done' status should record durations.""" |
| from obliteratus.abliterate import AbliterationPipeline |
|
|
| p = AbliterationPipeline.__new__(AbliterationPipeline) |
| p._stage_durations = {} |
| p._excise_modified_count = None |
| p._on_stage = lambda r: None |
|
|
| p._emit("summon", "running", "loading...", duration=0) |
| assert p._stage_durations == {} |
|
|
|
|
| |
|
|
|
|
| class TestStorageHelpers: |
| """Test persistent storage helper functions.""" |
|
|
| def test_test_writable_valid_dir(self): |
| with tempfile.TemporaryDirectory() as d: |
| assert _test_writable(Path(d) / "subdir") |
|
|
| def test_test_writable_unwritable(self): |
| |
| assert not _test_writable(Path("/proc/obliteratus_test")) |
|
|
| def test_is_mount_point_existing_path(self): |
| |
| result = _is_mount_point(Path("/")) |
| assert isinstance(result, bool) |
|
|
| def test_is_mount_point_nonexistent(self): |
| assert not _is_mount_point(Path("/nonexistent_dir_12345")) |
|
|
| def test_storage_diagnostic_returns_dict(self): |
| diag = storage_diagnostic() |
| assert isinstance(diag, dict) |
| assert "telemetry_dir" in diag |
| assert "is_persistent" in diag |
| assert "on_hf_spaces" in diag |
| assert "telemetry_enabled" in diag |
| assert "data_dir_exists" in diag |
|
|
|
|
| |
|
|
|
|
| class TestHubRestore: |
| """Test Hub-to-local restore functionality.""" |
|
|
| def setup_method(self): |
| _reset_telemetry() |
| |
| import obliteratus.telemetry as t |
| t._restore_done = False |
|
|
| def test_restore_skips_when_no_repo(self): |
| with patch("obliteratus.telemetry._TELEMETRY_REPO", ""): |
| assert restore_from_hub() == 0 |
|
|
| def test_restore_deduplicates(self): |
| """Records already in local JSONL should not be re-added.""" |
| import obliteratus.telemetry as t |
|
|
| with tempfile.TemporaryDirectory() as d: |
| test_file = Path(d) / "telemetry.jsonl" |
| existing = {"session_id": "abc", "timestamp": "2025-01-01T00:00:00"} |
| test_file.write_text(json.dumps(existing) + "\n") |
|
|
| old_file = t.TELEMETRY_FILE |
| old_repo = t._TELEMETRY_REPO |
| t.TELEMETRY_FILE = test_file |
| t._TELEMETRY_REPO = "test/repo" |
| t._restore_done = False |
|
|
| try: |
| hub_records = [ |
| {"session_id": "abc", "timestamp": "2025-01-01T00:00:00"}, |
| {"session_id": "def", "timestamp": "2025-01-02T00:00:00"}, |
| ] |
| with patch("obliteratus.telemetry.fetch_hub_records", return_value=hub_records): |
| count = restore_from_hub() |
| assert count == 1 |
|
|
| |
| lines = test_file.read_text().strip().split("\n") |
| assert len(lines) == 2 |
| finally: |
| t.TELEMETRY_FILE = old_file |
| t._TELEMETRY_REPO = old_repo |
|
|
| def test_restore_only_runs_once(self): |
| """Calling restore_from_hub() twice should be a no-op the second time.""" |
| import obliteratus.telemetry as t |
| t._restore_done = False |
|
|
| with patch("obliteratus.telemetry._TELEMETRY_REPO", "test/repo"): |
| with patch("obliteratus.telemetry.fetch_hub_records", return_value=[]): |
| restore_from_hub() |
| |
| assert restore_from_hub() == 0 |
|
|