import math

import torch
from torch import nn


class CNNTransformer(nn.Module):
    def __init__(self,
                 input_dim,
                 output_dim,
                 seq_len,
                 embed_dim,
                 num_enc_layers,
                 num_heads):
        super().__init__()
        self.conv = nn.Sequential(
            nn.Conv1d(in_channels=input_dim, out_channels=input_dim, kernel_size=9, stride=2),  # 288 → ~140
            nn.ReLU(),
            nn.AdaptiveAvgPool1d(output_size=128),  # force to 128
            nn.Conv1d(input_dim, input_dim, kernel_size=5, stride=2),  # 128 → ~62
            nn.ReLU(),
            nn.AdaptiveAvgPool1d(output_size=48),  # final fixed length
        )

        # linear projection to embed dim
        self.input_proj = nn.Linear(input_dim, embed_dim)

        # Compute positional embedding ONCE at init
        pe = self._get_sinusoidal_embedding(seq_len, embed_dim)  # (seq_len, embed_dim)
        self.register_buffer('pos_embed', pe.unsqueeze(0))  # (1, seq_len, embed_dim)

        encoder_layer = nn.TransformerEncoderLayer(embed_dim, num_heads, dropout=0.1)
        self.encoder = nn.TransformerEncoder(encoder_layer, num_enc_layers)
        self.pool = nn.AdaptiveAvgPool1d(1)
        self.head = nn.Linear(embed_dim, output_dim)

    def _get_sinusoidal_embedding(self, seq_len, embed_dim):
        position = torch.arange(0, seq_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, embed_dim, 2) * -(math.log(10000.0) / embed_dim))
        pe = torch.zeros(seq_len, embed_dim)
        pe[:, 0::2] = torch.sin(position * div_term)
        pe[:, 1::2] = torch.cos(position * div_term)
        return pe  # (seq_len, embed_dim)

    def forward(self, x):
        # x: (B, seq_len, input_dim)
        x = x.permute(0, 2, 1)  # → (B, input_dim, seq_len)
        cnn_out = self.conv(x)  # → (B, input_dim, 48)

        cnn_out = cnn_out.transpose(1, 2)  # → (B, 48, input_dim)
        transformer_in = self.input_proj(cnn_out)  # → (B, 48, embed_dim)

        transformer_in = transformer_in.transpose(0, 1)  # → (48, B, embed_dim)

        # Add positional embedding
        pos_embed = self.pos_embed[:, :transformer_in.size(0), :]  # (1, seq_len, embed_dim)
        pos_embed = pos_embed.transpose(0, 1)  # → (seq_len, 1, embed_dim)
        transformer_in = transformer_in + pos_embed  # broadcast over batch

        # Apply Transformer encoder
        enc = self.encoder(transformer_in)  # (S, B, E)
        pooled = enc.mean(0)  # (B, E)
        return self.head(pooled)