"""
Unit tests for Advanced Signature Patterns - Phase 3A

Test Coverage:
1. Compositional patterns (chain multiple research techniques)
2. Hierarchical patterns (multi-level workflow composition)
3. Adaptive patterns (dynamic workflow adjustment)
4. Meta-learning patterns (learn from execution history)

CRITICAL: Write tests FIRST, then implement!
"""

import pytest

# Check if research dependencies are available
try:
    import arxiv

    RESEARCH_DEPS_AVAILABLE = True
except ImportError:
    RESEARCH_DEPS_AVAILABLE = False

pytestmark = pytest.mark.skipif(
    not RESEARCH_DEPS_AVAILABLE,
    reason="Research dependencies (arxiv) not installed",
)


class TestCompositionalPatterns:
    """Test compositional pattern creation and execution."""

    def test_create_compositional_pattern(self):
        """Test creating a compositional pattern from multiple research features."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()

        # Create pattern from multiple features
        pattern = builder.compose(
            features=["flash-attention", "maml", "tree-of-thought"],
            strategy="sequential",
        )

        assert pattern is not None
        assert pattern.num_components == 3
        assert pattern.strategy == "sequential"

    def test_compositional_pattern_execution(self):
        """Test executing a compositional pattern."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.compose(
            features=["feature-1", "feature-2"], strategy="sequential"
        )

        result = pattern.execute(input_data={"query": "test"})

        assert result is not None
        assert "output" in result
        assert result["execution_order"] == ["feature-1", "feature-2"]

    def test_compositional_pattern_parallel_strategy(self):
        """Test compositional pattern with parallel execution."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.compose(
            features=["feature-1", "feature-2", "feature-3"], strategy="parallel"
        )

        result = pattern.execute(input_data={"query": "test"})

        assert result is not None
        assert pattern.strategy == "parallel"
        assert len(result["outputs"]) == 3

    def test_compositional_pattern_ensemble_strategy(self):
        """Test compositional pattern with ensemble voting."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.compose(
            features=["feature-1", "feature-2", "feature-3"], strategy="ensemble"
        )

        result = pattern.execute(input_data={"query": "test"})

        assert result is not None
        assert "consensus_output" in result
        assert "confidence" in result


class TestHierarchicalPatterns:
    """Test hierarchical pattern composition."""

    def test_create_hierarchical_pattern(self):
        """Test creating multi-level hierarchical patterns."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()

        # Create hierarchical structure
        pattern = builder.hierarchical(
            levels=[
                ["preprocessing-feature"],
                ["analysis-feature-1", "analysis-feature-2"],
                ["synthesis-feature"],
            ]
        )

        assert pattern is not None
        assert pattern.num_levels == 3
        assert pattern.get_level_features(0) == ["preprocessing-feature"]

    def test_hierarchical_pattern_execution_flow(self):
        """Test hierarchical pattern executes in correct order."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.hierarchical(
            levels=[["level-0"], ["level-1-a", "level-1-b"], ["level-2"]]
        )

        result = pattern.execute(input_data={"query": "test"})

        assert result is not None
        assert result["execution_levels"] == [0, 1, 2]
        assert "level-2" in result["final_output"]

    def test_hierarchical_pattern_data_flow(self):
        """Test data flows correctly through hierarchy levels."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.hierarchical(levels=[["feature-a"], ["feature-b"]])

        result = pattern.execute(input_data={"initial": "data"})

        # Level 0 output should be input to level 1
        assert result["level_0_output"] == result["level_1_input"]


class TestAdaptivePatterns:
    """Test adaptive pattern behavior."""

    def test_create_adaptive_pattern(self):
        """Test creating adaptive pattern with performance monitoring."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()

        pattern = builder.adaptive(
            base_features=["feature-1", "feature-2"],
            adaptation_strategy="performance_based",
        )

        assert pattern is not None
        assert pattern.adaptation_strategy == "performance_based"
        assert pattern.can_adapt is True

    def test_adaptive_pattern_switches_based_on_performance(self):
        """Test adaptive pattern switches features based on performance."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.adaptive(
            base_features=["slow-feature", "fast-feature"],
            adaptation_strategy="performance_based",
        )

        # Execute multiple times
        results = []
        for i in range(3):
            result = pattern.execute(input_data={"query": f"test-{i}"})
            results.append(result["selected_feature"])

        # Should adapt to faster feature
        assert "fast-feature" in results

    def test_adaptive_pattern_accuracy_based_switching(self):
        """Test adaptive pattern switches based on accuracy."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.adaptive(
            base_features=["accurate-feature", "inaccurate-feature"],
            adaptation_strategy="accuracy_based",
        )

        result = pattern.execute(input_data={"query": "test"})

        assert result is not None
        assert pattern.get_adaptation_history() is not None

    def test_adaptive_pattern_fallback_mechanism(self):
        """Test adaptive pattern falls back on feature failure."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.adaptive(
            base_features=["primary", "fallback"], adaptation_strategy="fault_tolerant"
        )

        result = pattern.execute(input_data={"query": "test"})

        assert result is not None
        assert "feature_used" in result


class TestMetaLearningPatterns:
    """Test meta-learning pattern capabilities."""

    def test_create_meta_learning_pattern(self):
        """Test creating meta-learning pattern."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()

        pattern = builder.meta_learning(
            candidate_features=["feature-1", "feature-2", "feature-3"],
            learning_strategy="bandit",
        )

        assert pattern is not None
        assert pattern.learning_strategy == "bandit"
        assert len(pattern.candidate_features) == 3

    def test_meta_learning_pattern_learns_from_execution(self):
        """Test meta-learning pattern improves over time."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.meta_learning(
            candidate_features=["feature-1", "feature-2"], learning_strategy="bandit"
        )

        # Execute multiple times with feedback
        for i in range(10):
            result = pattern.execute(input_data={"query": f"test-{i}"})
            pattern.provide_feedback(result["execution_id"], reward=0.8)

        # Should have learned preferences
        stats = pattern.get_learning_stats()
        assert stats["total_executions"] == 10
        assert "feature_preferences" in stats

    def test_meta_learning_pattern_bandit_strategy(self):
        """Test meta-learning with multi-armed bandit."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.meta_learning(
            candidate_features=["f1", "f2", "f3"],
            learning_strategy="bandit",
            exploration_rate=0.1,
        )

        result = pattern.execute(input_data={"query": "test"})

        assert result is not None
        assert result["selected_feature"] in ["f1", "f2", "f3"]
        assert "exploration" in result or "exploitation" in result

    def test_meta_learning_pattern_gradient_based_strategy(self):
        """Test meta-learning with gradient-based optimization."""
        from kaizen.research import AdvancedPatternBuilder

        builder = AdvancedPatternBuilder()
        pattern = builder.meta_learning(
            candidate_features=["feature-1", "feature-2"], learning_strategy="gradient"
        )

        # Execute with feedback
        for i in range(5):
            result = pattern.execute(input_data={"query": f"test-{i}"})
            pattern.provide_feedback(result["execution_id"], reward=0.9)

        # Should have updated weights
        weights = pattern.get_feature_weights()
        assert len(weights) == 2
        assert all(w >= 0 for w in weights.values())


class TestPatternIntegration:
    """Test integration with Phase 1 & 2 components."""

    def test_patterns_use_research_registry(self):
        """Test patterns discover features from ResearchRegistry."""
        from kaizen.research import AdvancedPatternBuilder, ResearchRegistry

        registry = ResearchRegistry()
        builder = AdvancedPatternBuilder(registry=registry)

        # Should auto-discover features
        available_features = builder.get_available_features()

        assert isinstance(available_features, list)

    def test_patterns_integrate_with_feature_manager(self):
        """Test patterns work with FeatureManager."""
        from kaizen.research import (
            AdvancedPatternBuilder,
            FeatureManager,
            ResearchRegistry,
        )

        registry = ResearchRegistry()
        manager = FeatureManager(registry)
        builder = AdvancedPatternBuilder(feature_manager=manager)

        pattern = builder.compose(
            features=["feature-1", "feature-2"], strategy="sequential"
        )

        assert pattern is not None
