"""Metadata embeddings - SinusoidalEmbedding + MLPs for metadata conditioning."""

import torch
import torch.nn as nn


class SinusoidalEmbedding(nn.Module):
    """Sinusoidal embedding for metadata."""

    def __init__(self, max_value, embedding_dim):
        super().__init__()
        self.max_value = max_value
        self.embedding_dim = embedding_dim
        self.omega = 10000.0

    def forward(self, k):
        device = k.device
        k_normalized = k * self.max_value
        embedding = torch.zeros(
            (k.size(0), k.size(1), self.embedding_dim),
            device=device,
            dtype=k.dtype,
        )
        for j in range(k.size(1)):
            for i in range(self.embedding_dim // 2):
                omega_term = self.omega ** (-2 * i / self.embedding_dim)
                embedding[:, j, 2 * i] = torch.sin(k_normalized[:, j] * omega_term)
                embedding[:, j, 2 * i + 1] = torch.cos(k_normalized[:, j] * omega_term)
        return embedding.view(k.size(0), -1)


def create_condition_vector(embedded_metadata, mlp_models, embedding_dim):
    """Create condition vector from metadata embeddings and MLPs."""
    metadata_embeddings = [
        mlp_models[j](embedded_metadata[:, j * embedding_dim : (j + 1) * embedding_dim])
        for j in range(len(mlp_models))
    ]
    return sum(metadata_embeddings)


class MetadataMLP(nn.Module):
    def __init__(self, input_dim, embedding_dim):
        super().__init__()
        self.fc1 = nn.Linear(input_dim, embedding_dim)

    def forward(self, x):
        return self.fc1(x)


class MetadataEmbeddings(nn.Module):
    """Metadata embeddings - SinusoidalEmbedding + MLPs."""

    def __init__(self, max_value, embedding_dim, max_period, metadata_dim):
        super().__init__()
        self.sinusoidal_embedding = SinusoidalEmbedding(max_value, embedding_dim)
        self.mlp_models = nn.ModuleList([
            MetadataMLP(embedding_dim, embedding_dim * 4)
            for _ in range(metadata_dim)
        ])
        self.max_period = max_period
        self.embedding_dim = embedding_dim
        self.metadata_dim = metadata_dim
        self.max_value = max_value

    def forward(self, metadata=None):
        while isinstance(metadata, (list, tuple)) and len(metadata) == 1:
            metadata = metadata[0]
        if metadata.dim() == 1:
            metadata = metadata.unsqueeze(0)
        embedded_metadata = self.sinusoidal_embedding(metadata)
        return create_condition_vector(
            embedded_metadata, self.mlp_models, self.embedding_dim
        )


# Alias for config compatibility
metadata_embeddings = MetadataEmbeddings