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.no_grad()
def generate_response():
    model.eval()
    seed = torch.randint(0, model.head.out_features, (1, 1), device=DEVICE)
    input_ids = seed
    output_tokens = []

    for _ in range(50):  # Max 50 tokens (short sentences)
        output = model(input_ids)
        next_token_logits = output[:, -1, :] / 0.8  # temperature 0.8

        # Top-K Sampling
        top_k = 40
        values, indices = torch.topk(next_token_logits, k=top_k)
        probs = F.softmax(values, dim=-1)
        sampled_idx = torch.multinomial(probs, num_samples=1)

        next_token = indices.gather(-1, sampled_idx)

        output_tokens.append(next_token.item())

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

        # Break if punctuation (end of sentence)
        word = tokenizer.detokenize(next_token.item())
        if word in [".", "!", "?"]:
            break

    return tokenizer.detokenize(output_tokens)


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)
        record_to_journal(sentence)
        train_on_message(sentence)

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