import torch
import torch.nn as nn
import random
from model.tokenizer import Tokenizer
import torch.nn.functional as F
from model.memory import save_dream
from model.train import train_on_message


recent_dreams = []

DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = Tokenizer()
VOCAB_SIZE = 10000  # Temporary cap, grows dynamically
EMBED_DIM = 128


class MultiHeadSelfAttention(nn.Module):
    def __init__(self, embed_dim, heads):
        super().__init__()
        assert embed_dim % heads == 0
        self.heads = heads
        self.head_dim = embed_dim // heads
        self.scale = torch.sqrt(torch.tensor(self.head_dim, dtype=torch.float32))

        self.to_qkv = nn.Linear(embed_dim, embed_dim * 3)
        self.out = nn.Linear(embed_dim, embed_dim)

    def forward(self, x):
        B, T, C = x.shape
        qkv = self.to_qkv(x).view(B, T, self.heads, 3 * self.head_dim)
        q, k, v = qkv.chunk(3, dim=-1)

        attn_scores = (q @ k.transpose(-2, -1)) / self.scale
        attn_weights = torch.softmax(attn_scores, dim=-1)

        out = attn_weights @ v
        out = out.transpose(1, 2).contiguous().view(B, T, C)
        return self.out(out)


class TransformerBlock(nn.Module):
    def __init__(self, embed_dim, heads):
        super().__init__()
        self.attn = MultiHeadSelfAttention(embed_dim, heads)
        self.norm1 = nn.LayerNorm(embed_dim)
        self.ff = nn.Sequential(
            nn.Linear(embed_dim, embed_dim * 4),
            nn.ReLU(),
            nn.Linear(embed_dim * 4, embed_dim)
        )
        self.norm2 = nn.LayerNorm(embed_dim)

    def forward(self, x):
        x = x + self.attn(self.norm1(x))
        x = x + self.ff(self.norm2(x))
        return x


class TinyTransformer(nn.Module):
    def __init__(self, vocab_size=VOCAB_SIZE, embed_dim=256, depth=4, heads=8):
        super().__init__()
        self.token_embed = nn.Embedding(vocab_size, embed_dim)
        self.pos_embed = nn.Parameter(torch.randn(1, 128, embed_dim))
        self.blocks = nn.Sequential(*[TransformerBlock(embed_dim, heads) for _ in range(depth)])
        self.norm = nn.LayerNorm(embed_dim)
        self.head = nn.Linear(embed_dim, vocab_size)

    def forward(self, x):
        B, T = x.shape
        tok = self.token_embed(x)
        pos = self.pos_embed[:, :T, :]
        x = tok + pos
        x = self.blocks(x)
        x = self.norm(x)
        return self.head(x)


model = TinyTransformer().to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
loss_fn = nn.CrossEntropyLoss()


def generate_response():
    seed = torch.tensor([random.randint(0, tokenizer.next_id - 1)], device=DEVICE)
    output = model(seed.unsqueeze(0))
    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)
        train_on_message(sentence)
        recent_dreams.append((score, sentence))
        if len(recent_dreams) > 10:
            recent_dreams.pop(0)