"""
Tests for evidence-based strategy v2 functionality.

Tests cover:
- Evidence claim extraction
- Source verification
- Confidence scoring
- Contradiction detection
"""


class TestEvidenceClaimExtraction:
    """Tests for evidence claim extraction."""

    def test_evidence_claim_extraction(self):
        """Claims are extracted from sources."""

        claims = [
            "Climate change causes rising sea levels",
            "Rising sea levels lead to coastal flooding",
        ]

        assert len(claims) == 2

    def test_evidence_claim_deduplication(self):
        """Duplicate claims are removed."""
        claims = [
            "Climate change is real",
            "climate change is real",
            "Climate change is happening",
        ]

        unique_claims = list(set(c.lower() for c in claims))

        assert len(unique_claims) == 2

    def test_evidence_claim_categorization(self):
        """Claims are categorized by type."""
        claims = [
            {"text": "Study shows X", "type": "research"},
            {"text": "Experts say Y", "type": "opinion"},
            {"text": "Data indicates Z", "type": "data"},
        ]

        by_type = {}
        for claim in claims:
            t = claim["type"]
            if t not in by_type:
                by_type[t] = []
            by_type[t].append(claim)

        assert len(by_type) == 3

    def test_evidence_empty_source(self):
        """Empty sources return no claims."""
        source_text = ""

        if not source_text.strip():
            claims = []
        else:
            claims = ["some claim"]

        assert claims == []


class TestSourceVerification:
    """Tests for source verification."""

    def test_evidence_source_verification(self):
        """Sources are verified for credibility."""
        sources = [
            {"url": "https://nature.com/article", "domain": "nature.com"},
            {
                "url": "https://random-blog.com/post",
                "domain": "random-blog.com",
            },
        ]

        trusted_domains = {"nature.com", "science.org", "gov.uk"}

        verified = [s for s in sources if s["domain"] in trusted_domains]

        assert len(verified) == 1

    def test_evidence_source_authority_scoring(self):
        """Sources are scored by authority."""
        authority_scores = {
            "nature.com": 0.95,
            "wikipedia.org": 0.75,
            "random-blog.com": 0.30,
        }

        domain = "nature.com"
        score = authority_scores.get(domain, 0.5)

        assert score == 0.95

    def test_evidence_source_recency_weighting(self):
        """Recent sources are weighted higher."""
        from datetime import datetime, timedelta

        now = datetime.now()
        sources = [
            {"date": now - timedelta(days=30), "score": 0.9},
            {"date": now - timedelta(days=365), "score": 0.9},
            {"date": now - timedelta(days=730), "score": 0.9},
        ]

        # Apply recency decay
        for source in sources:
            days_old = (now - source["date"]).days
            decay = max(0.5, 1.0 - (days_old / 365 * 0.3))
            source["adjusted_score"] = source["score"] * decay

        # Most recent should have highest score
        assert sources[0]["adjusted_score"] > sources[2]["adjusted_score"]


class TestConfidenceScoring:
    """Tests for confidence scoring."""

    def test_evidence_confidence_scoring(self):
        """Claims are scored by confidence."""
        claim = {
            "text": "Climate change is accelerating",
            "sources": 5,
            "agreement_rate": 0.8,
        }

        confidence = claim["agreement_rate"] * min(1.0, claim["sources"] / 3)

        assert confidence >= 0.8

    def test_evidence_confidence_low_sources(self):
        """Low source count reduces confidence."""
        sources = 1

        source_factor = min(1.0, sources / 3)

        assert source_factor < 1.0

    def test_evidence_confidence_high_agreement(self):
        """High agreement increases confidence."""
        agreement_rate = 0.95

        confidence_boost = 1.0 + (agreement_rate - 0.5) * 0.2

        assert confidence_boost > 1.0

    def test_evidence_confidence_aggregation(self):
        """Confidence scores are aggregated."""
        claim_scores = [0.8, 0.9, 0.7, 0.85]

        avg_confidence = sum(claim_scores) / len(claim_scores)

        assert 0.8 <= avg_confidence <= 0.85


class TestContradictionDetection:
    """Tests for contradiction detection."""

    def test_evidence_contradiction_detection(self):
        """Contradictions between claims are detected."""
        claims = [
            {"text": "X increases Y", "source": "source1"},
            {"text": "X decreases Y", "source": "source2"},
        ]

        # Simple contradiction detection
        contradictions = []
        if (
            "increases" in claims[0]["text"]
            and "decreases" in claims[1]["text"]
        ):
            if claims[0]["text"].split()[0] == claims[1]["text"].split()[0]:
                contradictions.append((claims[0], claims[1]))

        assert len(contradictions) == 1

    def test_evidence_contradiction_resolution(self):
        """Contradictions are resolved by source weight."""
        claims = [
            {"text": "X is true", "weight": 0.9},
            {"text": "X is false", "weight": 0.6},
        ]

        # Higher weight wins
        resolved = max(claims, key=lambda c: c["weight"])

        assert resolved["text"] == "X is true"

    def test_evidence_no_contradictions(self):
        """Non-contradictory claims pass through."""

        contradictions = []  # No overlap

        assert len(contradictions) == 0


class TestConsensusAnalysis:
    """Tests for consensus analysis."""

    def test_evidence_consensus_analysis(self):
        """Consensus among sources is analyzed."""
        source_opinions = [
            {"position": "agree"},
            {"position": "agree"},
            {"position": "agree"},
            {"position": "disagree"},
        ]

        agree_count = sum(
            1 for s in source_opinions if s["position"] == "agree"
        )
        consensus = agree_count / len(source_opinions)

        assert consensus == 0.75

    def test_evidence_consensus_strong(self):
        """Strong consensus is detected."""
        consensus = 0.90

        if consensus >= 0.8:
            strength = "strong"
        elif consensus >= 0.6:
            strength = "moderate"
        else:
            strength = "weak"

        assert strength == "strong"

    def test_evidence_consensus_weak(self):
        """Weak consensus is detected."""
        consensus = 0.55

        if consensus >= 0.8:
            strength = "strong"
        elif consensus >= 0.6:
            strength = "moderate"
        else:
            strength = "weak"

        assert strength == "weak"


class TestCitationTracking:
    """Tests for citation tracking."""

    def test_evidence_citation_tracking(self):
        """Citations are tracked per claim."""
        claim = {
            "text": "Climate change is real",
            "citations": [
                {"source": "NASA", "year": 2023},
                {"source": "IPCC", "year": 2022},
            ],
        }

        citation_count = len(claim["citations"])

        assert citation_count == 2

    def test_evidence_citation_formatting(self):
        """Citations are formatted correctly."""
        citation = {"author": "Smith", "year": 2023, "title": "Study X"}

        formatted = (
            f"{citation['author']} ({citation['year']}). {citation['title']}"
        )

        assert formatted == "Smith (2023). Study X"

    def test_evidence_citation_deduplication(self):
        """Duplicate citations are removed."""
        citations = [
            {"source": "NASA", "year": 2023},
            {"source": "NASA", "year": 2023},
            {"source": "IPCC", "year": 2022},
        ]

        unique = []
        seen = set()
        for c in citations:
            key = (c["source"], c["year"])
            if key not in seen:
                seen.add(key)
                unique.append(c)

        assert len(unique) == 2


class TestQualityAssessment:
    """Tests for evidence quality assessment."""

    def test_evidence_quality_assessment(self):
        """Evidence quality is assessed."""
        evidence = {
            "source_count": 5,
            "avg_authority": 0.85,
            "consensus": 0.90,
            "recency_score": 0.80,
        }

        quality = (
            evidence["avg_authority"] * 0.3
            + evidence["consensus"] * 0.3
            + evidence["recency_score"] * 0.2
            + min(1.0, evidence["source_count"] / 5) * 0.2
        )

        assert quality > 0.8

    def test_evidence_quality_low_sources(self):
        """Low source count reduces quality."""
        source_count = 1
        quality_factor = min(1.0, source_count / 5)

        assert quality_factor == 0.2

    def test_evidence_quality_high_authority(self):
        """High authority sources increase quality."""
        authority_scores = [0.95, 0.90, 0.85]

        avg_authority = sum(authority_scores) / len(authority_scores)

        assert avg_authority == 0.9


class TestSynthesisGeneration:
    """Tests for evidence synthesis generation."""

    def test_evidence_synthesis_generation(self):
        """Synthesis is generated from evidence."""
        claims = [
            {"text": "A causes B", "confidence": 0.9},
            {"text": "B leads to C", "confidence": 0.85},
        ]

        # Simple synthesis
        synthesis = " Additionally, ".join(c["text"] for c in claims)

        assert "A causes B" in synthesis
        assert "B leads to C" in synthesis

    def test_evidence_synthesis_weighted(self):
        """Synthesis prioritizes high-confidence claims."""
        claims = [
            {"text": "High confidence claim", "confidence": 0.95},
            {"text": "Low confidence claim", "confidence": 0.40},
        ]

        # Filter low confidence
        high_conf = [c for c in claims if c["confidence"] >= 0.7]

        assert len(high_conf) == 1

    def test_evidence_synthesis_empty_claims(self):
        """Empty claims produce empty synthesis."""
        claims = []

        if not claims:
            synthesis = "No evidence found."
        else:
            synthesis = " ".join(c["text"] for c in claims)

        assert synthesis == "No evidence found."


class TestSearchIteration:
    """Tests for iterative evidence search."""

    def test_evidence_search_iteration(self):
        """Evidence search iterates until threshold met."""
        confidence_threshold = 0.8
        iterations = 0
        max_iterations = 5
        current_confidence = 0.4

        while (
            current_confidence < confidence_threshold
            and iterations < max_iterations
        ):
            iterations += 1
            current_confidence += 0.2  # Simulate improvement

        assert current_confidence >= confidence_threshold
        assert iterations == 2

    def test_evidence_search_max_iterations(self):
        """Search stops at max iterations."""
        max_iterations = 5
        iterations = 0

        while iterations < max_iterations:
            iterations += 1

        assert iterations == max_iterations

    def test_evidence_result_merging(self):
        """Results from iterations are merged."""
        iteration_results = [
            [{"claim": "A"}],
            [{"claim": "B"}, {"claim": "C"}],
        ]

        merged = []
        for results in iteration_results:
            merged.extend(results)

        assert len(merged) == 3


class TestErrorHandling:
    """Tests for evidence strategy error handling."""

    def test_evidence_error_handling(self):
        """Errors in evidence gathering are handled."""
        errors = []

        try:
            raise ConnectionError("Source unavailable")
        except ConnectionError as e:
            errors.append(str(e))

        assert len(errors) == 1

    def test_evidence_partial_failure(self):
        """Partial failures don't stop processing."""
        sources = ["source1", "source2", "source3"]
        results = []
        errors = []

        for source in sources:
            try:
                if source == "source2":
                    raise Exception("Failed")
                results.append({"source": source, "data": "ok"})
            except Exception:
                errors.append(source)

        assert len(results) == 2
        assert len(errors) == 1

    def test_evidence_llm_failure_graceful_degradation(self):
        """LLM failure degrades gracefully."""
        llm_available = False

        if llm_available:
            synthesis = "LLM synthesis"
        else:
            synthesis = "Simple concatenation of claims"

        assert "concatenation" in synthesis


class TestGetSynonymsExceptionHandling:
    """Tests for _get_synonyms exception handling (PR #2020).

    PR #2020 changed bare `except:` to `except Exception:` in _get_synonyms
    to catch any exception from the LLM call and return an empty list.
    """

    def test_get_synonyms_returns_list_on_success(self):
        """_get_synonyms should return list of synonyms on success."""
        from unittest.mock import MagicMock

        from local_deep_research.advanced_search_system.strategies.evidence_based_strategy_v2 import (
            EnhancedEvidenceBasedStrategy,
        )

        mock_model = MagicMock()
        mock_model.invoke.return_value = MagicMock(
            content="synonym1\nsynonym2\nsynonym3"
        )
        mock_search = MagicMock()

        strategy = EnhancedEvidenceBasedStrategy(
            model=mock_model,
            search=mock_search,
            all_links_of_system=[],
        )

        result = strategy._get_synonyms("test term")
        assert isinstance(result, list)
        assert len(result) <= 3  # Limited to 3 synonyms

    def test_get_synonyms_returns_empty_on_llm_error(self):
        """_get_synonyms should return empty list when LLM raises an error."""
        from unittest.mock import MagicMock

        from local_deep_research.advanced_search_system.strategies.evidence_based_strategy_v2 import (
            EnhancedEvidenceBasedStrategy,
        )

        mock_model = MagicMock()
        mock_model.invoke.side_effect = Exception("LLM unavailable")
        mock_search = MagicMock()

        strategy = EnhancedEvidenceBasedStrategy(
            model=mock_model,
            search=mock_search,
            all_links_of_system=[],
        )

        result = strategy._get_synonyms("test term")
        assert result == []
