"""
PixelArtGen — Text Encoder

A small transformer encoder that converts text prompts into
contextual embeddings for conditioning the pixel art decoder.
"""

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import List


class TextTokenizer:
    """Simple word-level tokenizer for text prompts."""

    def __init__(self, vocab: List[str]):
        self.word2idx = {w: i for i, w in enumerate(vocab)}
        self.idx2word = {i: w for i, w in enumerate(vocab)}
        self.pad_idx = self.word2idx.get("<pad>", 0)
        self.sos_idx = self.word2idx.get("<sos>", 1)
        self.eos_idx = self.word2idx.get("<eos>", 2)
        self.unk_idx = self.word2idx.get("<unk>", 3)
        self.vocab_size = len(vocab)

    def encode(self, text: str, max_len: int = 32) -> torch.Tensor:
        """Tokenize and pad a text prompt."""
        words = text.lower().strip().split()
        tokens = [self.sos_idx]
        for w in words:
            tokens.append(self.word2idx.get(w, self.unk_idx))
        tokens.append(self.eos_idx)

        # Pad or truncate
        if len(tokens) > max_len:
            tokens = tokens[:max_len]
        else:
            tokens += [self.pad_idx] * (max_len - len(tokens))

        return torch.tensor(tokens, dtype=torch.long)

    def encode_batch(self, texts: List[str], max_len: int = 32) -> torch.Tensor:
        """Encode a batch of text prompts."""
        return torch.stack([self.encode(t, max_len) for t in texts])


class TextEncoder(nn.Module):
    """
    Small transformer encoder for text prompts.
    
    Architecture:
    - Word embeddings + sinusoidal positional encoding
    - N transformer encoder layers with multi-head attention
    - Output: sequence of contextual embeddings (batch, seq_len, d_model)
    """

    def __init__(
        self,
        vocab_size: int,
        d_model: int = 256,
        nhead: int = 4,
        num_layers: int = 3,
        dim_feedforward: int = 512,
        max_seq_len: int = 32,
        dropout: float = 0.1,
    ):
        super().__init__()
        self.d_model = d_model
        self.embedding = nn.Embedding(vocab_size, d_model, padding_idx=0)
        self.pos_encoding = SinusoidalPositionalEncoding(d_model, max_seq_len)
        self.dropout = nn.Dropout(dropout)

        encoder_layer = nn.TransformerEncoderLayer(
            d_model=d_model,
            nhead=nhead,
            dim_feedforward=dim_feedforward,
            dropout=dropout,
            batch_first=True,
            norm_first=True,
        )
        self.transformer = nn.TransformerEncoder(encoder_layer, num_layers=num_layers)
        self.norm = nn.LayerNorm(d_model)

    def forward(self, text_tokens: torch.Tensor) -> torch.Tensor:
        """
        Args:
            text_tokens: (batch, seq_len) long tensor of word indices
        Returns:
            (batch, seq_len, d_model) contextual embeddings
        """
        # Create padding mask (True = ignore)
        pad_mask = (text_tokens == 0)  # pad_idx = 0

        # Embed + positional encode
        x = self.embedding(text_tokens) * math.sqrt(self.d_model)
        x = self.pos_encoding(x)
        x = self.dropout(x)

        # Transformer encode
        x = self.transformer(x, src_key_padding_mask=pad_mask)
        x = self.norm(x)

        return x


class SinusoidalPositionalEncoding(nn.Module):
    """Standard sinusoidal positional encoding."""

    def __init__(self, d_model: int, max_len: int = 512):
        super().__init__()
        pe = torch.zeros(max_len, d_model)
        position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))

        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        pe = pe.unsqueeze(0)  # (1, max_len, d_model)
        self.register_buffer("pe", pe)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        return x + self.pe[:, :x.size(1)]