import random
import torch
import torch.nn.functional as F
from model.memory import save_dream
from model.brain_state import model, tokenizer, DEVICE

recent_dreams = []


def generate_response():
    model.eval()
    # Pick a real known word to seed from context memory
    context_texts = get_recent_context(5)
    if context_texts:
        start = random.choice(context_texts)
        seed_tokens = tokenizer.tokenize(start)
        if seed_tokens:
            seed = torch.tensor([seed_tokens[-1]], device=DEVICE).unsqueeze(0)
        else:
            seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
    else:
        seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)

    output = model(seed)
    pred = torch.argmax(output, dim=-1).squeeze().tolist()

    if not isinstance(pred, list):
        pred = [pred]

    return tokenizer.detokenize(pred)


def score_sentence(sentence: str) -> float:
    words = sentence.strip().split()
    length = len(words)
    diversity = len(set(words)) / (length + 1)
    if length < 4:
        return 0.0
    return diversity * min(length, 20)


def daydream():
    model.eval()
    seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE).unsqueeze(0)
    dream = []

    for _ in range(12):
        out = model(seed)
        logits = out[:, -1, :]
        probs = F.softmax(logits, dim=-1)
        token = torch.multinomial(probs, num_samples=1)
        dream.append(token.item())
        seed = torch.cat([seed, token], dim=1)

    sentence = tokenizer.detokenize(dream)
    score = score_sentence(sentence)

    if score > 0.45:
        save_dream(sentence, score)
        from model.journal import record_to_journal
        record_to_journal(sentence)
        from model.trainer import train_on_message
        train_on_message(sentence)

    if len(recent_dreams) > 10:
        recent_dreams.pop(0)