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():
    model.eval()

    # Start from an empty input: purely organic thought
    input_ids = torch.zeros((1, 1), dtype=torch.long, device=DEVICE)

    output_tokens = []
    max_length = 50

    for _ in range(max_length):
        output = model(input_ids)
        next_token_logits = output[:, -1, :]
        next_token = torch.argmax(next_token_logits, dim=-1)

        # Get token id values for special tokens
        pad_token_id = tokenizer.vocab.get("<pad>", None)
        unk_token_id = tokenizer.vocab.get("<unk>", None)

        # Stop if the model predicts <pad> or <unk>
        if pad_token_id is not None and next_token.item() == pad_token_id:
            break
        if unk_token_id is not None and next_token.item() == unk_token_id:
            break

        output_tokens.append(next_token.item())
        input_ids = torch.cat([input_ids, next_token.unsqueeze(0)], dim=1)

    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)