"""
Viraltest — Edge Case & Scenario Tests (Daily Plan Format)
Runs scenarios for all 3 tasks using the new daily step format.
Each step = one full day. Agent submits a sparse daily plan.
"""

import random as stdlib_random
from typing import Callable, Dict, List, Tuple

from models import ScheduledAction, ViraltestAction
from server.viraltest_environment import (
    TAG_POOL,
    ViraltestEnvironment,
    ViraltestObservation,
)

TASKS = ["weekly_engage", "weekly_strategic", "weekly_competitive"]
SEED = 42

_CONTENT_TYPES = ["reel", "carousel", "story", "text_post"]
_TOPICS = ["AI tools", "fitness routine", "growth hacks", "travel guide", "food recipe", "wellness tips"]
_rng = stdlib_random.Random(99)


def _plan(actions: list) -> ViraltestAction:
    return ViraltestAction(scheduled_actions=[ScheduledAction(**a) for a in actions])


def run_episode(
    task: str,
    plan_fn: Callable[[Dict, int], ViraltestAction],
    label: str,
) -> float:
    env = ViraltestEnvironment()
    obs = env.reset(task=task, seed=SEED)
    obs_dict = obs.model_dump()
    rewards: List[float] = []
    min_energy = 1.0
    burned_out = False

    for day in range(1, 8):
        action = plan_fn(obs_dict, day)
        obs = env.step(action)
        obs_dict = obs.model_dump()
        r = obs.reward if obs.reward is not None else 0.0
        rewards.append(r)
        min_energy = min(min_energy, obs.creator_energy)
        if obs.done and obs.creator_energy <= 0:
            burned_out = True
        if obs.done:
            break

    score = (obs.metadata or {}).get("grader_score", 0.0)
    total_steps = len(rewards)

    print(f"  Task: {task}")
    print(f"  Days: {total_steps} | Done: {obs.done} | Burned out: {burned_out}")
    print(f"  Score: {score:.4f} | Total reward: {sum(rewards):.2f} | Avg reward: {sum(rewards)/len(rewards):.3f}")
    print(f"  Energy: {obs.creator_energy:.2f} | Min energy: {min_energy:.2f}")
    print(f"  Followers: {obs.follower_count} (started 10000, delta {obs.follower_count - 10000:+d})")
    print(f"  Engagement rate: {obs.engagement_rate:.4f}")
    print(f"  Unique tags: {len(obs.tag_performance)}")
    print(f"  Niche saturation: {obs.niche_saturation:.3f}")
    print()
    return score


def plan_always_rest(obs: dict, day: int) -> ViraltestAction:
    return _plan([])


def plan_spam(obs: dict, day: int) -> ViraltestAction:
    return _plan([{"hour": h, "action_type": "post", "content_type": "reel",
                   "topic": "AI tools", "tags": ["ai"]} for h in range(24)])


def plan_smart(obs: dict, day: int) -> ViraltestAction:
    trending = (obs.get("trending_topics") or ["AI tools"])[0]
    t_tags = list((obs.get("trending_tags") or [])[:2])
    pool_tag = TAG_POOL[(day * 2) % len(TAG_POOL)]
    pool_tag2 = TAG_POOL[(day * 2 + 1) % len(TAG_POOL)]
    ct1 = _CONTENT_TYPES[(day * 2) % 4]
    ct2 = _CONTENT_TYPES[(day * 2 + 1) % 4]
    return _plan([
        {"hour": 8, "action_type": "create_content"},
        {"hour": 12, "action_type": "post", "content_type": ct1, "topic": trending, "tags": t_tags + [pool_tag]},
        {"hour": 19, "action_type": "post", "content_type": ct2, "topic": trending, "tags": t_tags + [pool_tag2]},
    ])


def plan_no_rest(obs: dict, day: int) -> ViraltestAction:
    actions = []
    for h in range(24):
        ct = _CONTENT_TYPES[h % 4]
        topic = _rng.choice(_TOPICS)
        tags = _rng.sample(TAG_POOL, 3)
        actions.append({"hour": h, "action_type": "post", "content_type": ct, "topic": topic, "tags": tags})
    return _plan(actions)


def plan_minimal(obs: dict, day: int) -> ViraltestAction:
    trending = (obs.get("trending_topics") or ["minimalism"])[0]
    tags = list((obs.get("trending_tags") or [])[:3])
    return _plan([
        {"hour": 12, "action_type": "post", "content_type": "carousel", "topic": trending, "tags": tags},
    ])


def plan_tag_explorer(obs: dict, day: int) -> ViraltestAction:
    trending = (obs.get("trending_topics") or ["devtools"])[0]
    start = (day * 6) % len(TAG_POOL)
    tags1 = [TAG_POOL[(start + i) % len(TAG_POOL)] for i in range(3)]
    tags2 = [TAG_POOL[(start + 3 + i) % len(TAG_POOL)] for i in range(3)]
    ct1 = _CONTENT_TYPES[(day * 2) % 4]
    ct2 = _CONTENT_TYPES[(day * 2 + 1) % 4]
    return _plan([
        {"hour": 10, "action_type": "post", "content_type": ct1, "topic": trending, "tags": tags1},
        {"hour": 18, "action_type": "post", "content_type": ct2, "topic": trending, "tags": tags2},
    ])


def plan_queue_optimizer(obs: dict, day: int) -> ViraltestAction:
    trending = (obs.get("trending_topics") or ["productivity"])[0]
    tags = list((obs.get("trending_tags") or [])[:2]) + ["growth"]
    queue = obs.get("content_queue_size", 0)
    if day < 3 or queue < 2:
        return _plan([
            {"hour": 8, "action_type": "create_content"},
            {"hour": 10, "action_type": "create_content"},
            {"hour": 14, "action_type": "create_content"},
        ])
    ct = _CONTENT_TYPES[day % 4]
    return _plan([
        {"hour": 12, "action_type": "post", "content_type": ct, "topic": trending, "tags": tags},
        {"hour": 19, "action_type": "post", "content_type": _CONTENT_TYPES[(day + 1) % 4], "topic": trending, "tags": tags},
    ])


def plan_double_peak(obs: dict, day: int) -> ViraltestAction:
    trending = (obs.get("trending_topics") or ["peak time content"])[0]
    tags = list((obs.get("trending_tags") or [])[:3])
    return _plan([
        {"hour": 9, "action_type": "post", "content_type": "reel", "topic": trending, "tags": tags},
        {"hour": 15, "action_type": "post", "content_type": "carousel", "topic": trending, "tags": tags},
    ])


def plan_random(obs: dict, day: int) -> ViraltestAction:
    actions = []
    for h in range(24):
        r = _rng.random()
        if r < 0.1:
            ct = _rng.choice(_CONTENT_TYPES)
            topic = _rng.choice(["random topic", "AI tools", "fitness", "travel"])
            tags = _rng.sample(TAG_POOL, 2)
            actions.append({"hour": h, "action_type": "post", "content_type": ct, "topic": topic, "tags": tags})
        elif r < 0.15:
            actions.append({"hour": h, "action_type": "create_content"})
    return _plan(actions)


SCENARIOS: List[Tuple[str, Callable, str]] = [
    ("Always Rest", plan_always_rest, "Zero engagement, no growth, energy stays max"),
    ("Spam Post", plan_spam, "Post every hour, burns out instantly"),
    ("Smart Agent", plan_smart, "Peak hours, trending, varied types, energy management"),
    ("No Rest", plan_no_rest, "Post every hour, never rests, burns out"),
    ("Minimal Poster", plan_minimal, "1 carousel at noon per day"),
    ("Tag Explorer", plan_tag_explorer, "Rotates through tag pool for max discovery"),
    ("Queue Optimizer", plan_queue_optimizer, "Creates content first, posts from queue"),
    ("Double Peak", plan_double_peak, "Posts at 9am and 3pm"),
    ("Random Actor", plan_random, "Random sparse actions each day"),
]


if __name__ == "__main__":
    print("=" * 70)
    print("VIRALTEST — DAILY PLAN SCENARIO TESTS")
    print("=" * 70)
    print()

    for scenario_name, plan_fn, description in SCENARIOS:
        print("=" * 70)
        print(f"{scenario_name}")
        print(f"  {description}")
        print("=" * 70)
        print()

        for task in TASKS:
            _rng = stdlib_random.Random(99)
            run_episode(task, plan_fn, scenario_name)

        print()

    print("=" * 70)
    print("SUMMARY TABLE")
    print("=" * 70)
    print()
    print(f"{'Scenario':<30} {'Engage':>8} {'Strategic':>10} {'Competitive':>12}")
    print("-" * 62)

    for scenario_name, plan_fn, _ in SCENARIOS:
        scores = []
        for task in TASKS:
            _rng = stdlib_random.Random(99)
            env = ViraltestEnvironment()
            obs = env.reset(task=task, seed=SEED)
            obs_dict = obs.model_dump()
            for day in range(1, 8):
                action = plan_fn(obs_dict, day)
                obs = env.step(action)
                obs_dict = obs.model_dump()
                if obs.done:
                    break
            scores.append((obs.metadata or {}).get("grader_score", 0.0))
        print(f"{scenario_name:<30} {scores[0]:>8.4f} {scores[1]:>10.4f} {scores[2]:>12.4f}")

    print()
    print("EXPECTED: Smart/Queue/Tag Explorer should score highest.")
    print("Burnout agents (spam, no_rest) should score near 0 on strategic/competitive.")