from typing import Dict, Any, List
from .models import NarrativeBlock, Source, Signal, Provenance
from datetime import datetime
import uuid


def news_adapter_event(headline: str, uri: str, confidence: float = 0.9) -> Dict[str, Any]:
    """Create a normalized event payload from a news headline. This is an adapter stub.

    In real usage this would parse entities, extract sentiment, etc. Here we keep a simple,
    deterministic mapping for deterministic replay testing.
    """
    return {
        "type": "news",
        "headline": headline,
        "uri": uri,
        "confidence": float(confidence),
    }


def transcript_adapter_event(speaker: str, text: str, ts: str) -> Dict[str, Any]:
    return {"type": "transcript", "speaker": speaker, "text": text, "ts": ts}


def build_block_from_events(events: List[Dict[str, Any]]) -> NarrativeBlock:
    """Deterministically build a NarrativeBlock from a list of events.

    This function demonstrates normalization logic: aggregating sources and signals.
    """
    # derive an id deterministically from the concatenated event payloads
    concat = "".join([str(e) for e in events])
    block_id = uuid.uuid5(uuid.NAMESPACE_URL, concat).hex
    topic = " | ".join(set([e.get("type") for e in events if e.get("type")]))
    ts = datetime.utcnow()
    sources = []
    signals = []
    sentiment_vals = []
    for e in events:
        if e.get("type") == "news":
            sources.append(Source(type="news", uri=e.get("uri"), confidence=e.get("confidence")))
            # naive deterministic sentiment from headline length
            sentiment_vals.append(len(e.get("headline", "")) % 5 - 2)
        if e.get("type") == "transcript":
            sources.append(Source(type="transcript", uri=f"transcript://{e.get('speaker')}", confidence=1.0))
            # a toy signal: word count
            signals.append(Signal(name="word_count", value=float(len(e.get("text", "").split())), provenance={"speaker": e.get("speaker")}))

    sentiment = None
    if sentiment_vals:
        sentiment = sum(sentiment_vals) / len(sentiment_vals)

    prov = Provenance(trace_id=block_id)

    block = NarrativeBlock(
        id=block_id,
        topic=topic,
        timestamp=ts,
        sources=sources,
        signals=signals,
        sentiment=sentiment,
        provenance=prov,
    )
    return block