# generator/templates_context.py

import random
from typing import List, Dict, Any

from .meaning_model import StructuredMeaning, Context
from .encoder import encode_term


def generate_context_rich_samples(
    dictionaries: Dict[str, Any],
    count: int,
    source_language: str = "en",
) -> List[Dict[str, Any]]:
    """
    Generates samples that combine:
    - actor
    - action
    - object
    - time context
    - place context
    - activity context
    """

    actions = dictionaries.get("actions", [])
    objects = dictionaries.get("objects", [])
    actors = dictionaries.get("actors", [])
    times = dictionaries.get("context_time", [])
    places = dictionaries.get("context_place", [])
    activities = dictionaries.get("context_activity", [])

    results = []

    if not actions or not objects or not actors:
        raise ValueError("Missing required dictionaries: actions, objects, actors.")

    for _ in range(count):
        action = random.choice(actions)
        obj = random.choice(objects)
        actor = random.choice(actors)

        time_ctx = random.choice(times) if times else None
        place_ctx = random.choice(places) if places else None
        activity_ctx = random.choice(activities) if activities else None

        action_id = action.get("id", "action.unknown")
        object_id = obj.get("id", "object.unknown")
        actor_id = actor.get("id", "actor.unknown")

        time_id = time_ctx.get("id") if time_ctx else None
        place_id = place_ctx.get("id") if place_ctx else None
        activity_id = activity_ctx.get("id") if activity_ctx else None

        input_text = f"{actor_id} {action_id} the {object_id}"
        if place_id:
            input_text += f" in the {place_id}"
        if activity_id:
            input_text += f" while {activity_id}"
        if time_id:
            input_text += f" ({time_id})"
        input_text += "."

        meaning = StructuredMeaning(
            actor=actor_id,
            action=action_id,
            object=object_id,
            modifiers=[],
            emotion=None,
            context=Context(
                time=time_id,
                place=place_id,
                activity=activity_id,
            ),
            intent="intent.describe_event",
            meta={
                "source_language": source_language,
                "confidence": 0.95,
            },
        )

        actor_enc = encode_term(actor_id, dictionaries)
        action_enc = encode_term(action_id, dictionaries)
        object_enc = encode_term(object_id, dictionaries)
        time_enc = encode_term(time_id, dictionaries) if time_id else None
        place_enc = encode_term(place_id, dictionaries) if place_id else None
        activity_enc = encode_term(activity_id, dictionaries) if activity_id else None

        glyphic_human = (
            f"ACTOR[{actor_enc['human']}] "
            f"ACTION[{action_enc['human']}] "
            f"OBJECT[{object_enc['human']}]"
        )
        if time_enc:
            glyphic_human += f" CONTEXT.TIME[{time_enc['human']}]"
        if place_enc:
            glyphic_human += f" CONTEXT.PLACE[{place_enc['human']}]"
        if activity_enc:
            glyphic_human += f" CONTEXT.ACTIVITY[{activity_enc['human']}]"
        glyphic_human += " INTENT.describe"

        glyphic_compact = (
            f"ACT{{{actor_enc['compact']}}} "
            f"ACTN{{{action_enc['compact']}}} "
            f"OBJ{{{object_enc['compact']}}}"
        )
        if time_enc:
            glyphic_compact += f" CTX{{TIME:{time_enc['compact']}}}"
        if place_enc:
            glyphic_compact += f" CTX{{PLACE:{place_enc['compact']}}}"
        if activity_enc:
            glyphic_compact += f" CTX{{ACT:{activity_enc['compact']}}}"
        glyphic_compact += " INT{DESC}"

        glyphic_tokens = (
            f"{actor_enc['tokens']} "
            f"{action_enc['tokens']} "
            f"{object_enc['tokens']}"
        )
        if time_enc:
            glyphic_tokens += f" <CTX:TIME:{time_enc['compact']}>"
        if place_enc:
            glyphic_tokens += f" <CTX:PLACE:{place_enc['compact']}>"
        if activity_enc:
            glyphic_tokens += f" <CTX:ACT:{activity_enc['compact']}>"
        glyphic_tokens += " <INT:DESC>"

        results.append(
            {
                "input_text": input_text,
                "glyphic_output_human": glyphic_human,
                "glyphic_output_compact": glyphic_compact,
                "glyphic_output_tokens": glyphic_tokens,
                "structured_meaning": meaning.to_dict(),
            }
        )

    return results