import torch
import torch.nn as nn
from open_af3.pairformer import PairFormer


class TemplateEmbedder(nn.Module):
    def __init__(
        self,
        dim: int = None,
        depth: int = 2,
        seq_len: int = None,
        heads: int = 64,
        dim_head: int = 64,
        attn_dropout: float = 0.0,
        ff_dropout: float = 0.0,
        global_column_attn: bool = False,
        c: int = 64,
        Ntemplates: int = 1,
        *args,
        **kwargs,
    ):
        super(TemplateEmbedder, self).__init__()
        # Define layers used in the embedding
        self.layer_norm_z = nn.LayerNorm(c)
        self.layer_norm_v = nn.LayerNorm(c)
        self.linear_no_bias_z = nn.Linear(c, c, bias=False)
        self.linear_no_bias_a = nn.Linear(c, c, bias=False)
        self.pairformer = PairFormer(
            dim=dim,
            seq_len=seq_len,
            heads=heads,
            dim_head=dim_head,
            attn_dropout=attn_dropout,
            ff_dropout=ff_dropout,
            depth=depth,
            *args,
            **kwargs,
        )
        self.relu = nn.ReLU()
        self.final_linear = nn.Linear(c, c, bias=False)

    def forward(self, f, zij, Ntemplates):
        # Step 1-3: Compute various masks and concatenate
        template_backbone_frame_mask = f  # Placeholder operation
        template_pseudo_beta_mask = f  # Placeholder operation
        template_distogram = f  # Placeholder operation
        template_unit_vector = f  # Placeholder operation

        atij = torch.cat(
            [
                template_distogram,
                template_backbone_frame_mask,
                template_unit_vector,
                template_pseudo_beta_mask,
            ],
            dim=-1,
        )

        # Step 4-5: Apply masking based on asym_id and concatenate restypes
        asym_id_mask = (
            f == f
        )  # Placeholder for actual asym_id comparison logic
        atij = atij * asym_id_mask
        restype = f  # Placeholder for restype feature
        atij = torch.cat([atij, restype, restype], dim=-1)

        # Initialize uij
        uij = torch.zeros_like(atij)

        # Step 7-11: Iterate over templates
        for _ in range(Ntemplates):
            vij = self.linear_no_bias_z(
                self.layer_norm_z(zij)
            ) + self.linear_no_bias_a(atij)
            for layer in self.pairformer_stack:
                vij = layer(
                    vij
                )  # Assuming some residual connection or similar logic in actual Pairformer
            uij += self.layer_norm_v(vij)

        # Step 12-13: Normalize and apply final transformation
        uij /= Ntemplates
        uij = self.final_linear(self.relu(uij))

        return uij
