"""Tests for the community contribution system."""

import json
from unittest.mock import MagicMock

import pytest
import torch

from obliteratus.community import (
    CONTRIBUTION_SCHEMA_VERSION,
    _config_fingerprint,
    _model_short_name,
    aggregate_results,
    generate_latex_table,
    load_contributions,
    save_contribution,
)


# ── Helper: mock pipeline ──────────────────────────────────────────────


def _make_mock_pipeline():
    """Build a mock pipeline with all fields the community 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 = 0.3
    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

    # Direction data
    d = torch.randn(4096)
    d = d / d.norm()
    p.refusal_directions = {10: d, 11: d + 0.01 * torch.randn(4096)}
    p.refusal_subspaces = {10: torch.randn(4, 4096)}

    # Excise details
    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 = []

    # Prompts
    p.harmful_prompts = ["x"] * 33
    p.harmless_prompts = ["y"] * 33
    p.jailbreak_prompts = None

    return p


# ── Model short name ───────────────────────────────────────────────────


class TestModelShortName:
    def test_strips_org_prefix(self):
        assert _model_short_name("meta-llama/Llama-2-7b-chat-hf") == "llama-2-7b-chat-hf"

    def test_no_org_prefix(self):
        assert _model_short_name("gpt2") == "gpt2"

    def test_sanitizes_special_chars(self):
        assert _model_short_name("org/Model_V2.1") == "model-v2-1"

    def test_caps_length(self):
        long_name = "a" * 100
        assert len(_model_short_name(long_name)) <= 60

    def test_collapses_dashes(self):
        assert _model_short_name("org/Model---Name") == "model-name"

    def test_strips_trailing_dashes(self):
        assert _model_short_name("org/Model-") == "model"


# ── Config fingerprint ─────────────────────────────────────────────────


class TestConfigFingerprint:
    def test_deterministic(self):
        config = {"n_directions": 4, "norm_preserve": True}
        fp1 = _config_fingerprint(config)
        fp2 = _config_fingerprint(config)
        assert fp1 == fp2

    def test_different_configs_different_hashes(self):
        fp1 = _config_fingerprint({"n_directions": 4})
        fp2 = _config_fingerprint({"n_directions": 8})
        assert fp1 != fp2

    def test_key_order_invariant(self):
        fp1 = _config_fingerprint({"a": 1, "b": 2})
        fp2 = _config_fingerprint({"b": 2, "a": 1})
        assert fp1 == fp2

    def test_returns_8_char_hex(self):
        fp = _config_fingerprint({"test": True})
        assert len(fp) == 8
        assert all(c in "0123456789abcdef" for c in fp)


# ── Save contribution ──────────────────────────────────────────────────


class TestSaveContribution:
    def test_saves_json_file(self, tmp_path):
        pipeline = _make_mock_pipeline()
        path = save_contribution(
            pipeline,
            model_name="meta-llama/Llama-2-7b-chat-hf",
            output_dir=tmp_path,
        )
        assert path.exists()
        assert path.suffix == ".json"
        data = json.loads(path.read_text())
        assert data["contribution_schema_version"] == CONTRIBUTION_SCHEMA_VERSION
        assert data["model_name"] == "meta-llama/Llama-2-7b-chat-hf"

    def test_filename_format(self, tmp_path):
        pipeline = _make_mock_pipeline()
        path = save_contribution(
            pipeline,
            model_name="meta-llama/Llama-2-7b-chat-hf",
            output_dir=tmp_path,
        )
        name = path.stem
        assert name.startswith("llama-2-7b-chat-hf_advanced_")

    def test_includes_telemetry_report(self, tmp_path):
        pipeline = _make_mock_pipeline()
        path = save_contribution(
            pipeline,
            model_name="meta-llama/Llama-2-7b-chat-hf",
            output_dir=tmp_path,
        )
        data = json.loads(path.read_text())
        telemetry = data["telemetry"]
        assert telemetry["schema_version"] == 2
        assert telemetry["model"]["architecture"] == "LlamaForCausalLM"
        assert telemetry["method"] == "advanced"
        assert telemetry["quality_metrics"]["refusal_rate"] == 0.05

    def test_includes_config_fingerprint(self, tmp_path):
        pipeline = _make_mock_pipeline()
        path = save_contribution(
            pipeline,
            model_name="meta-llama/Llama-2-7b-chat-hf",
            output_dir=tmp_path,
        )
        data = json.loads(path.read_text())
        assert "config_fingerprint" in data
        assert len(data["config_fingerprint"]) == 8

    def test_includes_notes(self, tmp_path):
        pipeline = _make_mock_pipeline()
        path = save_contribution(
            pipeline,
            model_name="test/model",
            notes="Ran on A100 with default prompts",
            output_dir=tmp_path,
        )
        data = json.loads(path.read_text())
        assert data["notes"] == "Ran on A100 with default prompts"

    def test_creates_output_dir(self, tmp_path):
        subdir = tmp_path / "nested" / "dir"
        assert not subdir.exists()
        pipeline = _make_mock_pipeline()
        path = save_contribution(
            pipeline, model_name="test/model", output_dir=subdir,
        )
        assert subdir.exists()
        assert path.exists()

    def test_timestamp_format(self, tmp_path):
        pipeline = _make_mock_pipeline()
        path = save_contribution(
            pipeline, model_name="test/model", output_dir=tmp_path,
        )
        data = json.loads(path.read_text())
        ts = data["timestamp"]
        # Should be UTC ISO-ish: YYYYMMDDTHHMMSSZ
        assert ts.endswith("Z")
        assert "T" in ts
        assert len(ts) == 16

    def test_method_config_extracted(self, tmp_path):
        pipeline = _make_mock_pipeline()
        path = save_contribution(
            pipeline, model_name="test/model", output_dir=tmp_path,
        )
        data = json.loads(path.read_text())
        cfg = data["telemetry"]["method_config"]
        assert cfg["n_directions"] == 4
        assert cfg["norm_preserve"] is True
        assert cfg["attention_head_surgery"] is True


# ── Load contributions ─────────────────────────────────────────────────


class TestLoadContributions:
    def _write_contrib(self, directory, model, method, refusal_rate, idx=0):
        """Write a minimal valid contribution file."""
        record = {
            "contribution_schema_version": CONTRIBUTION_SCHEMA_VERSION,
            "timestamp": f"20260227T{idx:06d}Z",
            "model_name": model,
            "config_fingerprint": "abcd1234",
            "notes": "",
            "telemetry": {
                "schema_version": 2,
                "method": method,
                "quality_metrics": {"refusal_rate": refusal_rate},
            },
        }
        path = directory / f"contrib_{idx}.json"
        path.write_text(json.dumps(record))
        return path

    def test_loads_valid_files(self, tmp_path):
        self._write_contrib(tmp_path, "test/model", "advanced", 0.05, 0)
        self._write_contrib(tmp_path, "test/model", "basic", 0.10, 1)
        records = load_contributions(tmp_path)
        assert len(records) == 2

    def test_sorts_by_timestamp(self, tmp_path):
        self._write_contrib(tmp_path, "model-b", "advanced", 0.05, 2)
        self._write_contrib(tmp_path, "model-a", "advanced", 0.10, 1)
        records = load_contributions(tmp_path)
        assert records[0]["model_name"] == "model-a"
        assert records[1]["model_name"] == "model-b"

    def test_skips_non_contribution_json(self, tmp_path):
        # Write a JSON file without contribution_schema_version
        (tmp_path / "random.json").write_text('{"foo": "bar"}')
        self._write_contrib(tmp_path, "test/model", "advanced", 0.05, 0)
        records = load_contributions(tmp_path)
        assert len(records) == 1

    def test_skips_invalid_json(self, tmp_path):
        (tmp_path / "bad.json").write_text("not valid json {{{")
        self._write_contrib(tmp_path, "test/model", "advanced", 0.05, 0)
        records = load_contributions(tmp_path)
        assert len(records) == 1

    def test_returns_empty_for_missing_dir(self, tmp_path):
        records = load_contributions(tmp_path / "nonexistent")
        assert records == []

    def test_tracks_source_file(self, tmp_path):
        self._write_contrib(tmp_path, "test/model", "advanced", 0.05, 0)
        records = load_contributions(tmp_path)
        assert "_source_file" in records[0]
        assert "contrib_0.json" in records[0]["_source_file"]

    def test_ignores_non_json_files(self, tmp_path):
        (tmp_path / "readme.txt").write_text("some text")
        self._write_contrib(tmp_path, "test/model", "advanced", 0.05, 0)
        records = load_contributions(tmp_path)
        assert len(records) == 1


# ── Aggregate results ──────────────────────────────────────────────────


class TestAggregateResults:
    def _make_record(self, model, method, refusal_rate, perplexity=None, coherence=None):
        metrics = {"refusal_rate": refusal_rate}
        if perplexity is not None:
            metrics["perplexity"] = perplexity
        if coherence is not None:
            metrics["coherence"] = coherence
        return {
            "model_name": model,
            "telemetry": {
                "method": method,
                "quality_metrics": metrics,
            },
        }

    def test_single_record(self):
        records = [self._make_record("model-a", "advanced", 0.05)]
        result = aggregate_results(records)
        assert "model-a" in result
        assert "advanced" in result["model-a"]
        assert result["model-a"]["advanced"]["n_runs"] == 1
        assert result["model-a"]["advanced"]["refusal_rate"]["mean"] == 0.05

    def test_multiple_runs_same_model_method(self):
        records = [
            self._make_record("model-a", "advanced", 0.04),
            self._make_record("model-a", "advanced", 0.06),
        ]
        result = aggregate_results(records)
        stats = result["model-a"]["advanced"]
        assert stats["n_runs"] == 2
        assert stats["refusal_rate"]["mean"] == 0.05
        assert stats["refusal_rate"]["min"] == 0.04
        assert stats["refusal_rate"]["max"] == 0.06
        assert stats["refusal_rate"]["n"] == 2

    def test_multiple_models(self):
        records = [
            self._make_record("model-a", "advanced", 0.05),
            self._make_record("model-b", "basic", 0.10),
        ]
        result = aggregate_results(records)
        assert len(result) == 2
        assert "model-a" in result
        assert "model-b" in result

    def test_multiple_methods(self):
        records = [
            self._make_record("model-a", "advanced", 0.05),
            self._make_record("model-a", "basic", 0.10),
        ]
        result = aggregate_results(records)
        assert len(result["model-a"]) == 2
        assert "advanced" in result["model-a"]
        assert "basic" in result["model-a"]

    def test_std_zero_for_single_run(self):
        records = [self._make_record("model-a", "advanced", 0.05)]
        result = aggregate_results(records)
        assert result["model-a"]["advanced"]["refusal_rate"]["std"] == 0.0

    def test_multiple_metrics(self):
        records = [
            self._make_record("model-a", "advanced", 0.05, perplexity=5.2, coherence=0.8),
        ]
        result = aggregate_results(records)
        stats = result["model-a"]["advanced"]
        assert "refusal_rate" in stats
        assert "perplexity" in stats
        assert "coherence" in stats
        assert stats["perplexity"]["mean"] == 5.2

    def test_missing_metric_skipped(self):
        records = [self._make_record("model-a", "advanced", 0.05)]
        result = aggregate_results(records)
        # coherence not provided, should not appear
        assert "coherence" not in result["model-a"]["advanced"]

    def test_unknown_model_and_method(self):
        records = [{
            "telemetry": {"quality_metrics": {"refusal_rate": 0.1}},
        }]
        result = aggregate_results(records)
        assert "unknown" in result
        assert "unknown" in result["unknown"]


# ── LaTeX table generation ─────────────────────────────────────────────


class TestGenerateLatexTable:
    def _sample_aggregated(self):
        return {
            "meta-llama/Llama-2-7b-chat-hf": {
                "advanced": {
                    "n_runs": 3,
                    "refusal_rate": {"mean": 0.04, "std": 0.01, "n": 3, "min": 0.03, "max": 0.05},
                },
                "basic": {
                    "n_runs": 2,
                    "refusal_rate": {"mean": 0.08, "std": 0.02, "n": 2, "min": 0.06, "max": 0.10},
                },
            },
            "mistralai/Mistral-7B-Instruct-v0.2": {
                "advanced": {
                    "n_runs": 1,
                    "refusal_rate": {"mean": 0.03, "std": 0.0, "n": 1, "min": 0.03, "max": 0.03},
                },
            },
        }

    def test_produces_valid_latex(self):
        agg = self._sample_aggregated()
        latex = generate_latex_table(agg)
        assert "\\begin{tabular}" in latex
        assert "\\end{tabular}" in latex
        assert "\\toprule" in latex
        assert "\\bottomrule" in latex

    def test_includes_model_names(self):
        agg = self._sample_aggregated()
        latex = generate_latex_table(agg)
        assert "Llama-2-7b-chat-hf" in latex
        assert "Mistral-7B-Instruct-v0.2" in latex

    def test_includes_method_headers(self):
        agg = self._sample_aggregated()
        latex = generate_latex_table(agg)
        assert "advanced" in latex
        assert "basic" in latex

    def test_missing_method_shows_dash(self):
        agg = self._sample_aggregated()
        latex = generate_latex_table(agg)
        # Mistral doesn't have "basic" method
        assert "---" in latex

    def test_shows_std_when_multiple_runs(self):
        agg = self._sample_aggregated()
        latex = generate_latex_table(agg)
        assert "$\\pm$" in latex

    def test_no_std_for_single_run(self):
        agg = {
            "model": {
                "method": {
                    "n_runs": 1,
                    "refusal_rate": {"mean": 0.03, "std": 0.0, "n": 1, "min": 0.03, "max": 0.03},
                },
            },
        }
        latex = generate_latex_table(agg)
        assert "$\\pm$" not in latex

    def test_methods_filter(self):
        agg = self._sample_aggregated()
        latex = generate_latex_table(agg, methods=["advanced"])
        assert "\\textbf{advanced}" in latex
        assert "\\textbf{basic}" not in latex

    def test_custom_metric(self):
        agg = {
            "model": {
                "method": {
                    "n_runs": 2,
                    "perplexity": {"mean": 5.2, "std": 0.3, "n": 2, "min": 4.9, "max": 5.5},
                },
            },
        }
        latex = generate_latex_table(agg, metric="perplexity")
        assert "5.2" in latex

    def test_column_count_matches_methods(self):
        agg = self._sample_aggregated()
        latex = generate_latex_table(agg)
        # 2 methods → "lcc" (1 model col + 2 method cols)
        assert "{@{}lcc@{}}" in latex


# ── CLI integration ────────────────────────────────────────────────────


class TestCLIContributeFlag:
    def test_contribute_flag_accepted(self):
        """Verify the --contribute flag parses without error."""
        from obliteratus.cli import main

        # We can't run the full command (no GPU), but verify parsing works
        with pytest.raises(SystemExit):
            # "obliterate" requires a model, so parse will fail,
            # but if --contribute is not recognized it fails differently
            main(["obliterate", "--help"])

    def test_aggregate_command_accepted(self):
        """Verify the aggregate command parses without error."""
        from obliteratus.cli import main

        with pytest.raises(SystemExit):
            main(["aggregate", "--help"])


# ── Package exports ────────────────────────────────────────────────────


class TestPackageExports:
    def test_save_contribution_importable(self):
        from obliteratus import save_contribution
        assert callable(save_contribution)

    def test_load_contributions_importable(self):
        from obliteratus import load_contributions
        assert callable(load_contributions)

    def test_aggregate_results_importable(self):
        from obliteratus import aggregate_results
        assert callable(aggregate_results)


# ── End-to-end: save → load → aggregate ───────────────────────────────


class TestEndToEnd:
    def test_save_load_aggregate_roundtrip(self, tmp_path):
        """Full roundtrip: save contributions, load them, aggregate."""
        pipeline = _make_mock_pipeline()

        # Save two contributions (different models to avoid filename collision)
        save_contribution(
            pipeline, model_name="test/model-a", output_dir=tmp_path,
        )
        # Tweak metrics for second run with a different model name
        pipeline._quality_metrics = {"perplexity": 5.5, "coherence": 0.75, "refusal_rate": 0.07}
        save_contribution(
            pipeline, model_name="test/model-b", output_dir=tmp_path,
        )

        # Load
        records = load_contributions(tmp_path)
        assert len(records) == 2

        # Aggregate
        aggregated = aggregate_results(records)
        assert "test/model-a" in aggregated
        assert "test/model-b" in aggregated
        stats_a = aggregated["test/model-a"]["advanced"]
        stats_b = aggregated["test/model-b"]["advanced"]
        assert stats_a["n_runs"] == 1
        assert stats_b["n_runs"] == 1
        assert abs(stats_a["refusal_rate"]["mean"] - 0.05) < 0.001
        assert abs(stats_b["refusal_rate"]["mean"] - 0.07) < 0.001

    def test_save_load_aggregate_to_latex(self, tmp_path):
        """Full roundtrip ending in LaTeX output."""
        pipeline = _make_mock_pipeline()
        save_contribution(
            pipeline, model_name="meta-llama/Llama-2-7b-chat-hf", output_dir=tmp_path,
        )

        records = load_contributions(tmp_path)
        aggregated = aggregate_results(records)
        latex = generate_latex_table(aggregated)

        assert "\\begin{tabular}" in latex
        assert "Llama-2-7b-chat-hf" in latex
        assert "advanced" in latex