from .base import BaseLanguageAgent
from ..normalizers import normalize_en_text
from ..system_strings import build_contextual_system_vocabulary, build_system_vocabulary


class EnglishAgent(BaseLanguageAgent):
    locale = "en"
    tone = "business-friendly and direct"
    preferred_formality = "neutral"
    vocabulary_map = {
        **build_system_vocabulary("en", ("plan_badge", "services_badge", "transparent_label", "transparent_investment")),
    }
    _system_contextual = build_contextual_system_vocabulary("en", ("plan_badge", "services_badge", "transparent_label"))
    contextual_vocabulary_map = {
        "badge": {**_system_contextual.get("badge", {})},
        "label": {**_system_contextual.get("label", {})},
        "metric": {**_system_contextual.get("metric", {})},
        "stat": {**_system_contextual.get("stat", {})},
        "title": {**_system_contextual.get("title", {})},
        "heading": {**_system_contextual.get("heading", {})},
        "rendered": {**_system_contextual.get("rendered", {})},
    }
    cta_defaults = {
        "starter": "Book starter call",
        "business": "Book business call",
        "support": "View support",
        "service": "View services",
        "project": "Start your project",
        "quote": "Request a quote",
        "contact": "Book intro call",
    }

    def post_cleanup_text(self, text: str, field_path: str = "") -> str:
        return normalize_en_text(text, field_path=field_path)