import math

import torch
from torch import nn
from torch.nn import functional as F

from .config import ModelConfig


class CausalSelfAttention(nn.Module):
    def __init__(self, config: ModelConfig):
        super().__init__()
        if config.n_embd % config.n_heads != 0:
            raise ValueError("n_embd must be divisible by n_heads.")

        self.n_heads = config.n_heads
        self.head_dim = config.n_embd // config.n_heads
        self.qkv = nn.Linear(config.n_embd, 3 * config.n_embd)
        self.proj = nn.Linear(config.n_embd, config.n_embd)
        self.dropout = nn.Dropout(config.dropout)
        mask = torch.tril(torch.ones(config.block_size, config.block_size))
        self.register_buffer("mask", mask.view(1, 1, config.block_size, config.block_size))

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        batch_size, seq_len, channels = x.shape
        qkv = self.qkv(x)
        q, k, v = qkv.split(channels, dim=2)

        q = q.view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        k = k.view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        v = v.view(batch_size, seq_len, self.n_heads, self.head_dim).transpose(1, 2)

        att = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
        att = att.masked_fill(self.mask[:, :, :seq_len, :seq_len] == 0, float("-inf"))
        att = F.softmax(att, dim=-1)
        att = self.dropout(att)

        y = att @ v
        y = y.transpose(1, 2).contiguous().view(batch_size, seq_len, channels)
        return self.proj(y)


class FeedForward(nn.Module):
    def __init__(self, config: ModelConfig):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(config.n_embd, 4 * config.n_embd),
            nn.GELU(),
            nn.Linear(4 * config.n_embd, config.n_embd),
            nn.Dropout(config.dropout),
        )

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return self.net(x)


class Block(nn.Module):
    def __init__(self, config: ModelConfig):
        super().__init__()
        self.ln1 = nn.LayerNorm(config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.ln2 = nn.LayerNorm(config.n_embd)
        self.ff = FeedForward(config)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x + self.attn(self.ln1(x))
        x = x + self.ff(self.ln2(x))
        return x


class TinyTransformerLM(nn.Module):
    def __init__(self, config: ModelConfig):
        super().__init__()
        self.config = config
        self.token_embedding = nn.Embedding(config.vocab_size, config.n_embd)
        self.position_embedding = nn.Embedding(config.block_size, config.n_embd)
        self.dropout = nn.Dropout(config.dropout)
        self.blocks = nn.ModuleList([Block(config) for _ in range(config.n_layers)])
        self.ln_f = nn.LayerNorm(config.n_embd)
        self.head = nn.Linear(config.n_embd, config.vocab_size)

    def forward(
        self,
        idx: torch.Tensor,
        targets: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor | None]:
        _, seq_len = idx.shape
        if seq_len > self.config.block_size:
            raise ValueError("Input sequence exceeds block size.")

        positions = torch.arange(0, seq_len, device=idx.device)
        x = self.token_embedding(idx) + self.position_embedding(positions)
        x = self.dropout(x)
        for block in self.blocks:
            x = block(x)
        x = self.ln_f(x)
        logits = self.head(x)

        loss = None
        if targets is not None:
            loss = F.cross_entropy(
                logits.view(-1, logits.size(-1)),
                targets.view(-1),
            )
        return logits, loss

    @torch.no_grad()
    def generate(
        self,
        idx: torch.Tensor,
        max_new_tokens: int,
        temperature: float = 1.0,
        top_k: int | None = None,
    ) -> torch.Tensor:
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -self.config.block_size :]
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :] / max(temperature, 1e-5)

            if top_k is not None:
                values, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < values[:, [-1]]] = float("-inf")

            probs = F.softmax(logits, dim=-1)
            next_token = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, next_token), dim=1)

        return idx