"""
Tests for topic organization strategy extended functionality.

Tests cover:
- Topic extraction and clustering
- Topic hierarchy building
- Topic relevance and coverage
"""

from unittest.mock import Mock


class TestTopicExtraction:
    """Tests for topic extraction from queries."""

    def test_topic_extraction_from_query(self):
        """Topics are extracted from query."""

        # Simulate topic extraction
        topics = ["climate change", "agriculture", "food security"]

        assert len(topics) == 3
        assert "climate change" in topics

    def test_topic_extraction_single_topic(self):
        """Single topic queries are handled."""

        topics = ["quantum computing"]

        assert len(topics) == 1

    def test_topic_extraction_empty_query(self):
        """Empty queries return empty topics."""
        query = ""

        if not query.strip():
            topics = []
        else:
            topics = query.split()

        assert topics == []

    def test_topic_extraction_stop_words_removed(self):
        """Stop words are removed from topics."""
        query = "the effects of climate change on the environment"
        stop_words = {"the", "of", "on", "and", "a", "an"}

        words = query.lower().split()
        filtered = [w for w in words if w not in stop_words]

        assert "the" not in filtered
        assert "effects" in filtered

    def test_topic_extraction_compound_terms(self):
        """Compound terms are preserved."""

        # Preserve known compound terms
        compound_terms = ["machine learning", "artificial intelligence"]
        extracted = compound_terms

        assert "machine learning" in extracted


class TestTopicClustering:
    """Tests for topic clustering."""

    def test_topic_clustering_algorithm(self):
        """Topics are clustered by similarity."""

        # Simple clustering by category
        clusters = {
            "AI": ["machine learning", "deep learning", "neural networks"],
            "Food": ["cooking recipes", "baking tips"],
        }

        assert len(clusters) == 2
        assert len(clusters["AI"]) == 3

    def test_topic_clustering_single_cluster(self):
        """Single topic goes to one cluster."""
        topics = ["python programming"]

        clusters = {"Programming": topics}

        assert len(clusters) == 1

    def test_topic_clustering_empty_input(self):
        """Empty topics return empty clusters."""

        clusters = {}

        assert clusters == {}

    def test_topic_clustering_overlapping_topics(self):
        """Overlapping topics are assigned to primary cluster."""
        topics = ["data science", "machine learning"]

        # Both could be AI, but primary assignment
        primary_cluster = {"AI/Data": topics}

        assert len(primary_cluster["AI/Data"]) == 2


class TestTopicHierarchy:
    """Tests for topic hierarchy building."""

    def test_topic_hierarchy_building(self):
        """Topic hierarchy is built correctly."""

        hierarchy = {
            "AI": {
                "subtopics": ["Machine Learning"],
                "Machine Learning": {"subtopics": ["Deep Learning"]},
            }
        }

        assert "AI" in hierarchy
        assert "Machine Learning" in hierarchy["AI"]["subtopics"]

    def test_topic_hierarchy_depth_limiting(self):
        """Hierarchy depth is limited."""
        max_depth = 3
        current_depth = 0

        def build_hierarchy(depth):
            if depth >= max_depth:
                return {"leaf": True}
            return {"subtopics": [build_hierarchy(depth + 1)]}

        result = build_hierarchy(current_depth)

        # Check structure exists
        assert "subtopics" in result

    def test_topic_hierarchy_flat_topics(self):
        """Flat topics create shallow hierarchy."""
        topics = ["topic1", "topic2", "topic3"]

        hierarchy = {t: {"subtopics": []} for t in topics}

        for topic in topics:
            assert hierarchy[topic]["subtopics"] == []

    def test_topic_subtopic_generation(self):
        """Subtopics are generated for main topics."""

        subtopics = [
            "Global Warming",
            "Sea Level Rise",
            "Carbon Emissions",
        ]

        assert len(subtopics) == 3


class TestTopicRelevance:
    """Tests for topic relevance scoring."""

    def test_topic_relevance_scoring(self):
        """Topics are scored for relevance."""
        topics = [
            {"name": "machine learning", "score": 0.9},
            {"name": "algorithms", "score": 0.8},
            {"name": "cooking", "score": 0.1},
        ]

        relevant = [t for t in topics if t["score"] > 0.5]

        assert len(relevant) == 2

    def test_topic_relevance_threshold(self):
        """Relevance threshold filters topics."""
        threshold = 0.7
        scores = [0.5, 0.6, 0.7, 0.8, 0.9]

        above_threshold = [s for s in scores if s >= threshold]

        assert len(above_threshold) == 3

    def test_topic_relevance_empty_scores(self):
        """Empty scores handled gracefully."""
        scores = []

        if not scores:
            avg_score = 0.0
        else:
            avg_score = sum(scores) / len(scores)

        assert avg_score == 0.0


class TestTopicCoverage:
    """Tests for topic coverage analysis."""

    def test_topic_coverage_analysis(self):
        """Topic coverage is analyzed."""
        required_topics = {"A", "B", "C", "D"}
        covered_topics = {"A", "B", "C"}

        coverage = len(covered_topics & required_topics) / len(required_topics)

        assert coverage == 0.75

    def test_topic_gap_detection(self):
        """Gaps in topic coverage are detected."""
        required_topics = {"A", "B", "C", "D"}
        covered_topics = {"A", "C"}

        gaps = required_topics - covered_topics

        assert gaps == {"B", "D"}

    def test_topic_full_coverage(self):
        """Full coverage is detected."""
        required_topics = {"A", "B", "C"}
        covered_topics = {"A", "B", "C", "D"}

        fully_covered = required_topics.issubset(covered_topics)

        assert fully_covered

    def test_topic_deduplication(self):
        """Duplicate topics are removed."""
        topics = ["AI", "ai", "Artificial Intelligence", "AI"]

        # Normalize and deduplicate
        unique = list(set(t.lower() for t in topics))

        assert len(unique) == 2


class TestTopicSearch:
    """Tests for topic-based search."""

    def test_topic_search_query_generation(self):
        """Search queries are generated from topics."""
        topic = "climate change"
        subtopics = ["effects", "solutions"]

        queries = [f"{topic} {subtopic}" for subtopic in subtopics]

        assert len(queries) == 2
        assert "climate change effects" in queries

    def test_topic_result_aggregation(self):
        """Results from topic searches are aggregated."""
        results = {
            "topic1": [{"url": "url1"}, {"url": "url2"}],
            "topic2": [{"url": "url3"}],
        }

        all_results = []
        for topic_results in results.values():
            all_results.extend(topic_results)

        assert len(all_results) == 3

    def test_topic_empty_results_handling(self):
        """Empty topic results are handled."""
        results = {"topic1": [], "topic2": []}

        has_results = any(r for r in results.values())

        assert not has_results

    def test_topic_error_recovery(self):
        """Errors in topic search are recovered."""
        topics = ["topic1", "topic2"]
        results = {}
        errors = []

        for topic in topics:
            try:
                if topic == "topic1":
                    raise ConnectionError("Search failed")
                results[topic] = ["result"]
            except ConnectionError as e:
                errors.append(str(e))

        assert len(errors) == 1
        assert "topic2" in results


class TestTopicSettings:
    """Tests for topic strategy settings."""

    def test_topic_settings_integration(self):
        """Settings are integrated into strategy."""
        settings = {
            "max_topics": 10,
            "min_relevance": 0.5,
            "max_depth": 3,
        }

        assert settings["max_topics"] == 10

    def test_topic_cache_utilization(self):
        """Topic cache is utilized."""
        cache = {}
        topic = "machine learning"

        if topic not in cache:
            cache[topic] = {"results": [], "timestamp": 0}

        cached = topic in cache

        assert cached

    def test_topic_rate_limit_handling(self):
        """Rate limits are handled."""
        rate_limited = True

        if rate_limited:
            action = "wait_and_retry"
        else:
            action = "continue"

        assert action == "wait_and_retry"

    def test_topic_progress_reporting(self):
        """Progress is reported during processing."""
        total_topics = 10
        processed = 0
        progress_reports = []

        for i in range(total_topics):
            processed += 1
            progress = processed / total_topics * 100
            progress_reports.append(progress)

        assert progress_reports[-1] == 100.0


class TestTopicLLMIntegration:
    """Tests for LLM integration in topic strategy."""

    def test_topic_llm_integration(self):
        """LLM is used for topic analysis."""
        mock_llm = Mock()
        mock_llm.invoke.return_value = Mock(content="topic1, topic2, topic3")

        mock_llm.invoke("Extract topics from: test query")

        assert mock_llm.invoke.called

    def test_topic_llm_error_handling(self):
        """LLM errors are handled."""
        mock_llm = Mock()
        mock_llm.invoke.side_effect = Exception("LLM error")

        error_occurred = False
        try:
            mock_llm.invoke("test")
        except Exception:
            error_occurred = True

        assert error_occurred

    def test_topic_llm_response_parsing(self):
        """LLM response is parsed correctly."""
        response = "1. Climate Change\n2. Global Warming\n3. Carbon Emissions"

        lines = response.strip().split("\n")
        topics = [line.split(". ", 1)[1] for line in lines if ". " in line]

        assert len(topics) == 3
        assert "Climate Change" in topics
