"""Tier 3: Emergence Tests (Stretch Goals)

These tests measure emergent capabilities.
Exploratory - no kill decision based solely on Tier 3.
"""

import pytest

from tribalmemory.interfaces import MemorySource


class TestCrossInstanceSynthesis:
    """T3.1: Cross-Instance Synthesis Tests
    
    Note: Full cross-instance tests require multi-instance deployment.
    These tests verify the foundation for synthesis.
    """
    
    @pytest.mark.tier3
    async def test_t3_1_1_knowledge_combination_foundation(self, memory_service):
        """T3.1.1: Foundation for combining knowledge from multiple sources."""
        # Simulate memories from different "instances" via tags
        await memory_service.remember(
            "Joe's coding style: prefer functional patterns",
            tags=["source:instance-0", "domain:coding"]
        )
        await memory_service.remember(
            "Joe's writing style: concise, uses bullet points",
            tags=["source:instance-1", "domain:writing"]
        )
        
        # Query that requires both
        results = await memory_service.recall(
            "Generate code documentation for Joe",
            limit=5
        )
        
        # Should retrieve memories from both domains
        all_content = " ".join(r.memory.content for r in results)
        
        has_coding = "coding" in all_content.lower() or "functional" in all_content.lower()
        has_writing = "writing" in all_content.lower() or "bullet" in all_content.lower()
        
        # At minimum, should get relevant memories; synthesis is higher-level
        assert len(results) >= 1, "Should retrieve at least one relevant memory"
    
    @pytest.mark.tier3
    @pytest.mark.edge_case
    async def test_t3_1_2_contradictory_cross_instance(self, memory_service):
        """T3.1.2: Handling contradictory info from different instances."""
        # Store contradictory preferences from different "instances"
        await memory_service.remember(
            "Joe prefers tabs for indentation",
            tags=["source:instance-0"]
        )
        await memory_service.remember(
            "Joe prefers spaces for indentation",
            tags=["source:instance-1"]
        )
        
        # Query about the contradiction
        results = await memory_service.recall("Tabs or spaces for Joe?", limit=5)
        
        # Should return both so conflict can be noted
        all_content = " ".join(r.memory.content for r in results)
        
        has_tabs = "tabs" in all_content.lower()
        has_spaces = "spaces" in all_content.lower()
        
        # Ideally both are returned so the conflict is surfaced
        # This is a stretch goal - at minimum, return something
        assert len(results) >= 1, "Should return at least one memory"


class TestLongitudinalLearning:
    """T3.2: Longitudinal Learning Tests
    
    These establish metrics for tracking improvement over time.
    Actual measurement requires 12-week data collection.
    """
    
    @pytest.mark.tier3
    async def test_t3_2_1_learning_curve_baseline(self, memory_service):
        """T3.2.1: Establish baseline for learning curve measurement."""
        # This test establishes what we'll measure over time:
        # 1. Corrections needed per interaction
        # 2. Relevant context surfacing rate
        # 3. Anticipatory actions
        
        # Store a sequence of memories
        memories = [
            "Project started on Monday",
            "Decided to use TypeScript",
            "Added Supabase integration",
            "Deployed to Vercel",
            "Fixed auth bug on Thursday",
        ]
        
        for m in memories:
            await memory_service.remember(m)
        
        # Measure recall effectiveness
        test_queries = [
            ("When did the project start?", ["Monday"]),
            ("What language are we using?", ["TypeScript"]),
            ("What's our backend?", ["Supabase"]),
        ]
        
        correct = 0
        for query, expected in test_queries:
            results = await memory_service.recall(query, limit=3)
            if results:
                content = " ".join(r.memory.content for r in results)
                if any(e in content for e in expected):
                    correct += 1
        
        accuracy = correct / len(test_queries)
        
        # This is baseline - we track improvement over time
        assert accuracy >= 0, "Baseline measurement should complete"
        
        # Log for longitudinal tracking
        # In production, this would write to EVALUATION.md
        print(f"Baseline accuracy: {accuracy:.1%} ({correct}/{len(test_queries)})")
