"""Tests for schema validation"""

import pytest
from sentiment_engine.schemas.payload import NormalizedPayload, SourceType, AssetMention, EngagementMetrics
from sentiment_engine.schemas.processed import ProcessedItem, EntityExtraction, SentimentScores, EmotionScores, EventClassification, EventType
from sentiment_engine.schemas.output import AssetSentiment, MarketSentiment, PumpDumpScore, VelocityMetrics, EventFlag


class TestPayloadSchemas:
    """Test payload schema validation"""

    def test_normalized_payload_valid(self):
        payload = NormalizedPayload(
            source_id="test",
            source_type=SourceType.NEWS,
            source_credibility_base=0.8,
            ingest_ts=1724262300.0,
            raw_text="BTC surges to new highs",
            asset_mentions=[AssetMention(asset_id="BTC", mention_span=(0, 3), confidence=0.9, source_text="BTC", mention_type="ticker")],
            content_length=25,
            language="en"
        )
        assert payload.source_id == "test"
        assert payload.has_assets is True
        assert payload.get_assets() == ["BTC"]

    def test_normalized_payload_empty_text_raises(self):
        with pytest.raises(ValueError, match="raw_text cannot be empty"):
            NormalizedPayload(
                source_id="test",
                source_type=SourceType.NEWS,
                source_credibility_base=0.8,
                ingest_ts=1724262300.0,
                raw_text="",
                content_length=0,
                language="en"
            )

    def test_engagement_metrics_total(self):
        metrics = EngagementMetrics(retweets=10, likes=50, replies=5, upvotes=100, comments=20)
        assert metrics.total_engagement() == 185


class TestProcessedSchemas:
    """Test processed item schemas"""

    def test_sentiment_scores_bounds(self):
        scores = SentimentScores(
            polarity=0.5,
            confidence=0.8,
            positive_prob=0.7,
            negative_prob=0.1,
            neutral_prob=0.2
        )
        assert -1.0 <= scores.polarity <= 1.0
        assert 0.0 <= scores.confidence <= 1.0

    def test_emotion_scores_bounds(self):
        emotions = EmotionScores(
            joy=0.8, fear=0.1, anger=0.05, greed=0.7, sadness=0.05, intensity=0.75
        )
        for val in [emotions.joy, emotions.fear, emotions.anger, emotions.greed, emotions.sadness, emotions.intensity]:
            assert 0.0 <= val <= 1.0

    def test_event_classification(self):
        event = EventClassification(
            event_type=EventType.LISTING,
            confidence=0.8,
            assets_involved=["BTC"],
            key_details={"exchange": "Binance"},
            severity=0.7
        )
        assert event.event_type == EventType.LISTING
        assert 0.0 <= event.severity <= 1.0


class TestOutputSchemas:
    """Test output schemas"""

    def test_asset_sentiment_acb_signals(self):
        asset = AssetSentiment(
            asset_id="BTC",
            fear_state=20.0,
            greed_state=80.0,
            sentiment_polarity=60.0,
            pump_dump=PumpDumpScore(asset_id="BTC", pump_score=75.0, dump_score=15.0, pump_confidence=0.8, dump_confidence=0.7, last_update_ts=1724262305.0),
            last_update_ts=1724262305.0
        )

        # Test ACB signal extraction
        market = MarketSentiment(
            fear_state=25.0,
            greed_state=75.0,
            sentiment_index=50.0,
            hype_velocity=65.0,
            pub_velocity=55.0,
            aggregate_pump_risk=75.0,
            aggregate_dump_risk=20.0,
            last_update_ts=1724262305.0
        )

        from sentiment_engine.schemas.output import SentimentOutput
        output = SentimentOutput(timestamp=1724262305.0, market=market, assets={"BTC": asset})

        acb = output.get_acb_signals()
        assert "market_sentiment_state" in acb
        assert "aggregate_pump_risk" in acb
        assert -1.0 <= acb["market_sentiment_state"] <= 1.0
        assert 0.0 <= acb["aggregate_pump_risk"] <= 1.0

    def test_book_health_veto(self):
        asset = AssetSentiment(
            asset_id="BTC",
            fear_state=20.0,
            greed_state=80.0,
            sentiment_polarity=60.0,
            pump_dump=PumpDumpScore(asset_id="BTC", pump_score=80.0, dump_score=15.0, pump_confidence=0.8, dump_confidence=0.7, last_update_ts=1724262305.0),
            last_update_ts=1724262305.0
        )

        from sentiment_engine.schemas.output import SentimentOutput, MarketSentiment
        market = MarketSentiment(
            fear_state=25.0, greed_state=75.0, sentiment_index=50.0,
            hype_velocity=65.0, pub_velocity=55.0,
            aggregate_pump_risk=75.0, aggregate_dump_risk=20.0,
            last_update_ts=1724262305.0
        )
        output = SentimentOutput(timestamp=1724262305.0, market=market, assets={"BTC": asset})

        veto = output.get_book_health_veto(threshold=75.0)
        assert "BTC" in veto

        veto_low = output.get_book_health_veto(threshold=85.0)
        assert "BTC" not in veto_low

    def test_exit_context(self):
        asset = AssetSentiment(
            asset_id="BTC",
            fear_state=85.0,
            greed_state=15.0,
            sentiment_polarity=-70.0,
            pump_dump=PumpDumpScore(asset_id="BTC", pump_score=10.0, dump_score=80.0, pump_confidence=0.8, dump_confidence=0.7, last_update_ts=1724262305.0),
            last_update_ts=1724262305.0
        )

        from sentiment_engine.schemas.output import SentimentOutput, MarketSentiment
        market = MarketSentiment(
            fear_state=80.0, greed_state=20.0, sentiment_index=-60.0,
            hype_velocity=30.0, pub_velocity=40.0,
            aggregate_pump_risk=15.0, aggregate_dump_risk=80.0,
            last_update_ts=1724262305.0
        )
        output = SentimentOutput(timestamp=1724262305.0, market=market, assets={"BTC": asset})

        ctx = output.get_exit_context(dump_threshold=70.0, fear_threshold=80.0)
        assert "BTC" in ctx["high_dump_assets"]
        assert "BTC" in ctx["high_fear_assets"]
        assert ctx["market_dump_risk"] == 80.0
        assert ctx["market_fear"] == 80.0