from torch import nn
from typing import Dict


class MLPProjectionHead(nn.Module):
    def __init__(self, embedding_dim, projection_dim, dropout):
        super().__init__()
        self.projection = nn.Linear(embedding_dim, projection_dim)
        self.gelu = nn.GELU()
        self.fc = nn.Linear(projection_dim, projection_dim)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(projection_dim)

    def forward(self, x):
        projected = self.projection(x)
        x = self.gelu(projected)
        x = self.fc(x)
        x = self.dropout(x)
        x = x + projected
        x = self.layer_norm(x)
        return x


class LinearProjectionHead(nn.Module):
    def __init__(self, embedding_dim, projection_dim):
        super().__init__()
        self.projection = nn.Linear(embedding_dim, projection_dim)

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


def load_projection_head(embedding_dim: int, config_projection_head: Dict):
    if config_projection_head["name"].lower() == "mlp":
        projection_head = MLPProjectionHead(
            embedding_dim=embedding_dim, projection_dim=config_projection_head["proj_dim"], dropout=config_projection_head["dropout"]
        )
    elif config_projection_head["name"].lower() == "linear":
        projection_head = LinearProjectionHead(embedding_dim=embedding_dim, projection_dim=config_projection_head["proj_dim"])
    else:
        raise KeyError(f"Not supported text encoder: {config_projection_head}")
    return projection_head