import random
import torch
import torch.nn.functional as F
from model.memory import save_dream
from model.brain_state import model, tokenizer, DEVICE
from model.journal import record_to_journal
from model.trainer import train_on_message
from context.context import get_recent_context

recent_dreams = []


@torch.inference_mode()
def generate_response(max_tokens: int = 50, temperature: float = 1.0):
    model.eval()
    input_ids = torch.tensor([tokenizer.token_to_id("<start>")], device=DEVICE).unsqueeze(0)

    generated = []

    forbidden_tokens = {
        tokenizer.token_to_id("<unk>"),
        tokenizer.token_to_id("<start>"),
        tokenizer.token_to_id("<pad>"),
        tokenizer.token_to_id("<end>"),
        tokenizer.token_to_id("<sep>"),
    }

    for _ in range(max_tokens):
        output = model(input_ids)
        if torch.isnan(output).any():
            print("[Brain] Detected NaN in output, restarting generation.")
            return "..."

        next_token_logits = output[:, -1, :]
        probs = torch.softmax(next_token_logits / temperature, dim=-1)

        next_token = torch.multinomial(probs, num_samples=1)

        # Resample if forbidden token
        while next_token.item() in forbidden_tokens:
            next_token = torch.multinomial(probs, num_samples=1)

        token_id = next_token.item()

        if tokenizer.reverse_vocab.get(token_id, "") == "<end>":
            break

        generated.append(token_id)

        input_ids = torch.cat([input_ids, next_token], dim=1)

    return tokenizer.detokenize(generated)


def score_sentence(sentence: str) -> float:
    words = sentence.strip().split()
    unique = set(words)
    length = len(words)
    unique_ratio = len(unique) / (length + 1)

    if length < 5:
        return 0.0
    if unique_ratio < 0.5:
        return 0.0

    return unique_ratio * min(length / 20.0, 1.0)


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 > 0.5:
        save_dream(sentence, score)
        record_to_journal(sentence)
        train_on_message(sentence)

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