# Copyright 2026 AlQuraishi Laboratory
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""
Sequence-local atom attention modules. Includes AtomAttentionEncoder,
AtomAttentionDecoder, and AtomTransformer.
"""

import torch
import torch.nn as nn
from ml_collections import ConfigDict

import openfold3.core.config.default_linear_init_config as lin_init
from openfold3.core.model.layers.diffusion_transformer import DiffusionTransformer
from openfold3.core.model.primitives import LayerNorm, Linear
from openfold3.core.utils.atom_attention_block_utils import (
    convert_pair_rep_to_blocks,
    convert_single_rep_to_blocks,
)
from openfold3.core.utils.atomize_utils import (
    aggregate_atom_feat_to_tokens,
    broadcast_token_feat_to_atoms,
)
from openfold3.core.utils.checkpointing import checkpoint_section

TensorDict = dict[str, torch.Tensor]


class RefAtomFeatureEmbedder(nn.Module):
    """
    Implements AF3 Algorithm 5 (line 1 - 6).
    """

    def __init__(
        self,
        c_atom_ref: ConfigDict,
        c_atom: int,
        c_atom_pair: int,
        linear_init_params: ConfigDict = lin_init.ref_atom_emb_init,
    ):
        """
        Args:
            c_atom_ref:
                Dict of reference atom channel dimensions per feature
            c_atom:
                Atom single conditioning channel dimension
            c_atom_pair:
                Atom pair conditioning channel dimension
            linear_init_params:
                Linear layer initialization parameters
        """
        super().__init__()
        # Ref conformer feats
        self.linear_ref_pos = Linear(3, c_atom, **linear_init_params.linear_feats)
        self.linear_ref_charge = Linear(1, c_atom, **linear_init_params.linear_feats)
        self.linear_ref_mask = Linear(1, c_atom, **linear_init_params.linear_feats)
        self.linear_ref_element = Linear(
            c_atom_ref.element, c_atom, **linear_init_params.linear_feats
        )
        self.linear_ref_atom_chars = Linear(
            c_atom_ref.name_chars, c_atom, **linear_init_params.linear_feats
        )
        self.linear_ref_offset = Linear(
            3, c_atom_pair, **linear_init_params.linear_ref_offset
        )
        self.linear_inv_sq_dists = Linear(
            1, c_atom_pair, **linear_init_params.linear_inv_sq_dists
        )
        self.linear_valid_mask = Linear(
            1, c_atom_pair, **linear_init_params.linear_valid_mask
        )

    def forward(
        self,
        batch: TensorDict,
        n_query: int,
        n_key: int,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """
        Args:
            batch:
                Input feature dictionary. Features used in this function:
                    - "ref_pos": [*, N_atom, 3] atom positions in the
                        reference conformer
                    - "ref_mask": [*, N_atom] atom mask for the reference conformer
                    - "ref_element": [*, N_atom, 128] one-hot encoding of atomic number
                        in the reference conformer
                    - "ref_charge": [*, N_atom] atom charge in the reference conformer
                    - "ref_atom_name_chars": [*, N_atom, 4, 64] one-hot encoding of
                        unicode integers representing unique atom names in the
                        reference conformer
                    - "ref_space_uid": [*, n_atom,] numerical encoding of the chain id
                        and residue index in the reference conformer
            n_query:
                Number of queries (block height)
            n_key:
                Number of keys (block width)
        Returns:
            cl:
                [*, N_atom, c_atom] Atom single conditioning
            plm:
                [*, N_blocks, N_query, N_key, c_atom_pair] Atom pair conditioning
        """
        dtype = batch["ref_pos"].dtype

        # Embed atom features
        # [*, N_atom, c_atom]
        cl = self.linear_ref_pos(batch["ref_pos"])
        cl = cl + self.linear_ref_charge(
            torch.arcsinh(batch["ref_charge"].unsqueeze(-1))
        )
        cl = cl + self.linear_ref_mask(batch["ref_mask"].unsqueeze(-1).to(dtype=dtype))
        cl = cl + self.linear_ref_element(batch["ref_element"].to(dtype=dtype))
        cl = cl + self.linear_ref_atom_chars(
            batch["ref_atom_name_chars"].flatten(start_dim=-2).to(dtype=dtype)
        )

        # Embed offsets
        # Convert all atom rep to block format ahead of time due to
        # reduce memory cost
        # dl, dm: [*, N_blocks, N_query, 3], [*, N_blocks, N_key, 3]
        # vl, vm: [*, N_blocks, N_query, 1], [*, N_blocks, N_key, 1]
        # atom_mask: [*, N_blocks, N_query, N_key]
        d_l, d_m, atom_mask = convert_single_rep_to_blocks(
            ql=batch["ref_pos"],
            n_query=n_query,
            n_key=n_key,
            atom_mask=batch["atom_mask"],
        )
        v_l, v_m, _ = convert_single_rep_to_blocks(
            ql=batch["ref_space_uid"].unsqueeze(-1),
            n_query=n_query,
            n_key=n_key,
            atom_mask=batch["atom_mask"],
        )

        # dlm: [*, N_blocks, N_query, N_key, 3]
        # vlm: [*, N_blocks, N_query, N_key, 1]
        dlm = (d_l.unsqueeze(-2) - d_m.unsqueeze(-3)) * atom_mask.unsqueeze(-1)
        vlm = (v_l.unsqueeze(-2) == v_m.unsqueeze(-3)).to(
            dtype=dlm.dtype
        ) * atom_mask.unsqueeze(-1)

        plm = self.linear_ref_offset(dlm) * vlm

        # Embed pairwise inverse squared distances
        # [*, N_blocks, N_query, N_key, c_atom_pair]
        inv_sq_dists = 1.0 / (1 + torch.sum(dlm**2, dim=-1, keepdim=True))
        plm = plm + self.linear_inv_sq_dists(inv_sq_dists) * vlm
        plm = plm + self.linear_valid_mask(vlm) * vlm

        return cl, plm


class NoisyPositionEmbedder(nn.Module):
    """
    Implements AF3 Algorithm 5 (line 8 - 12).
    """

    def __init__(
        self,
        c_s: int,
        c_z: int,
        c_atom: int,
        c_atom_pair: int,
        linear_init_params: ConfigDict = lin_init.noisy_pos_emb_init,
    ):
        """
        Args:
            c_s:
                Single representation channel dimension
            c_z:
                Pair representation channel dimension
            c_atom:
                Atom single conditioning channel dimension
            c_atom_pair:
                Atom pair conditioning channel dimension
            linear_init_params:
                Linear layer initialization parameters
        """
        super().__init__()
        self.layer_norm_s = LayerNorm(c_s, create_offset=False)
        self.linear_s = Linear(c_s, c_atom, **linear_init_params.linear_s)
        self.layer_norm_z = LayerNorm(c_z, create_offset=False)
        self.linear_z = Linear(c_z, c_atom_pair, **linear_init_params.linear_z)
        self.linear_r = Linear(3, c_atom, **linear_init_params.linear_r)

    def forward(
        self,
        batch: TensorDict,
        cl: torch.Tensor,
        plm: torch.Tensor,
        si_trunk: torch.Tensor,
        zij_trunk: torch.Tensor,
        rl: torch.Tensor,
        n_query: int,
        n_key: int,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Args:
            batch:
                Input feature dictionary. Features used in this function:
                    - "token_mask": [*, N_token] Token mask
                    - "num_atoms_per_token": [*, N_token] Number of atoms per token
            cl:
                [*, N_atom, c_atom] Atom single conditioning
            plm:
                [*, N_blocks, N_query, N_key, c_atom_pair] Atom pair conditioning
            si_trunk:
                [*, N_token, c_s] Trunk single representation
            zij_trunk:
                [*, N_token, N_token, c_z] Trunk pair representation
            rl:
                [*, N_atom, 3] Noisy atom positions
            n_query:
                Number of queries (block height)
            n_key:
                Number of keys (block width)
        Returns:
            cl:
                [*, N_atom, c_atom] Atom single conditioning with trunk single
                    representation embedded
            plm:
                [*, N_blocks, N_query, N_key, c_atom_pair] Atom pair conditioning with
                    trunk pair representation embedded
            ql:
                [*, N_atom, c_atom] Atom single representation with noisy coordinate
                    projection
        """

        # Broadcast trunk single representation into atom single conditioning
        # [*, N_atom, c_atom]
        si_trunk = self.linear_s(self.layer_norm_s(si_trunk))
        si_trunk = broadcast_token_feat_to_atoms(
            token_mask=batch["token_mask"],
            num_atoms_per_token=batch["num_atoms_per_token"],
            token_feat=si_trunk,
            token_dim=-2,
        )
        cl = cl + si_trunk

        # Broadcast trunk pair representation into atom pair conditioning
        zij_trunk = self.linear_z(self.layer_norm_z(zij_trunk))
        zij_trunk = convert_pair_rep_to_blocks(
            batch=batch, zij_trunk=zij_trunk, n_query=n_query, n_key=n_key
        )
        plm = plm + zij_trunk

        # Add noisy coordinate projection
        # [*, N_atom, c_atom]
        ql = cl + self.linear_r(rl)

        return cl, plm, ql


class AtomAttentionEncoder(nn.Module):
    """
    Implements AF3 Algorithm 5.
    """

    def __init__(
        self,
        c_atom_ref: ConfigDict,
        c_atom: int,
        c_atom_pair: int,
        c_token: int,
        add_noisy_pos: bool,
        c_hidden: int,
        no_heads: int,
        no_blocks: int,
        n_transition: int,
        n_query: int,
        n_key: int,
        use_ada_layer_norm: bool,
        c_s: int | None = None,
        c_z: int | None = None,
        blocks_per_ckpt: int | None = None,
        ckpt_intermediate_steps: bool = False,
        inf: float = 1e9,
        linear_init_params: ConfigDict = lin_init.atom_att_enc_init,
        use_reentrant: bool | None = None,
    ):
        """
        Args:
            c_atom_ref:
                Dict of reference atom channel dimensions per feature
            c_atom:
                Atom single representation channel dimension
            c_atom_pair:
                Atom pair representation channel dimension
            c_token:
                Token single representation channel dimension
            add_noisy_pos:
                Whether to add noisy positions and trunk embeddings
            c_hidden:
                Hidden channel dimension
            no_heads:
                Number of attention heads
            no_blocks:
                Number of attention blocks
            n_transition:
                Number of transition blocks
            n_query:
                Number of queries (block height)
            n_key:
                Number of keys (block width)
            use_ada_layer_norm:
                Whether to apply AdaLN-Zero conditioning
            c_s:
                Single representation channel dimension (optional)
            c_z:
                Pair representation channel dimension (optional)
            blocks_per_ckpt:
                Number of blocks per checkpoint. If set, checkpointing will
                be used to save memory.
            ckpt_intermediate_steps:
                Whether to checkpoint intermediate steps in the module, including
                RefAtomFeatureEmbedder, NoisyPositionEmbedder, and feature aggregation
            inf:
                Large number used for attention masking
            linear_init_params:
                Linear layer initialization parameters
            use_reentrant:
                Whether to use reentrant variant of checkpointing. If set,
                torch checkpointing will be used (DeepSpeed does not support
                this feature)
        """
        super().__init__()
        self.n_query = n_query
        self.n_key = n_key
        self.ckpt_intermediate_steps = ckpt_intermediate_steps
        self.inf = inf
        self.use_reentrant = use_reentrant

        self.ref_atom_feature_embedder = RefAtomFeatureEmbedder(
            c_atom_ref=c_atom_ref,
            c_atom=c_atom,
            c_atom_pair=c_atom_pair,
            linear_init_params=linear_init_params.ref_atom_emb,
        )

        if add_noisy_pos:
            self.noisy_position_embedder = NoisyPositionEmbedder(
                c_s=c_s,
                c_z=c_z,
                c_atom=c_atom,
                c_atom_pair=c_atom_pair,
                linear_init_params=linear_init_params.noisy_pos_emb,
            )

        self.relu = nn.ReLU()
        self.linear_l = Linear(c_atom, c_atom_pair, **linear_init_params.linear_l)
        self.linear_m = Linear(
            c_atom, c_atom_pair, **linear_init_params.linear_m
        )  # TODO: check initialization

        self.pair_mlp = nn.Sequential(
            nn.ReLU(),
            Linear(c_atom_pair, c_atom_pair, **linear_init_params.pair_mlp_1),
            nn.ReLU(),
            Linear(c_atom_pair, c_atom_pair, **linear_init_params.pair_mlp_2),
            nn.ReLU(),
            Linear(c_atom_pair, c_atom_pair, **linear_init_params.pair_mlp_3),
        )

        self.atom_transformer = DiffusionTransformer(
            c_a=c_atom,
            c_s=c_atom,
            c_z=c_atom_pair,
            c_hidden=c_hidden,
            no_heads=no_heads,
            no_blocks=no_blocks,
            n_transition=n_transition,
            use_ada_layer_norm=use_ada_layer_norm,
            n_query=self.n_query,
            n_key=self.n_key,
            blocks_per_ckpt=blocks_per_ckpt,
            inf=self.inf,
            linear_init_params=linear_init_params.diffusion_transformer,
            use_reentrant=use_reentrant,
        )

        self.c_token = c_token
        self.linear_q = nn.Sequential(
            Linear(c_atom, c_token, **linear_init_params.linear_q), nn.ReLU()
        )

    def get_atom_reps(
        self,
        batch: TensorDict,
        rl: torch.Tensor | None = None,
        si_trunk: torch.Tensor | None = None,
        zij_trunk: torch.Tensor | None = None,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Args:
            batch:
                Input feature dictionary
            rl:
                [*, N_atom, 3] Noisy atom positions (optional)
            si_trunk:
                [*, N_atom, c_s] Trunk single representation (optional)
            zij_trunk:
                [*, N_atom, N_atom, c_z] Trunk pair representation (optional)
        Returns:
            ql:
                [*, N_atom, c_atom] Atom single representation
            cl:
                [*, N_atom, c_atom] Atom single conditioning
            plm:
                [*, N_blocks, N_query, N_key, c_atom_pair] Atom pair representation
                Note: Converted to block format ahead of time due to reduce memory cost
        """
        # Embed reference atom features
        # cl: [*, N_atom, c_atom]
        # plm: [*, N_blocks, N_query, N_key, c_atom_pair]
        cl, plm = self.ref_atom_feature_embedder(
            batch=batch, n_query=self.n_query, n_key=self.n_key
        )

        # Embed noisy atom positions and trunk embeddings
        # cl: [*, N_atom, c_atom]
        # plm: [*, N_blocks, N_query, N_key, c_atom_pair]
        # ql: [*, N_atom, c_atom]
        if rl is not None:
            cl, plm, ql = self.noisy_position_embedder(
                batch=batch,
                cl=cl,
                plm=plm,
                si_trunk=si_trunk,
                zij_trunk=zij_trunk,
                rl=rl,
                n_query=self.n_query,
                n_key=self.n_key,
            )
        else:
            # Initialize atom single representation when trunk / noisy position
            # inputs are not present
            # [*, N_atom, c_atom]
            ql = cl.clone()

        # Add the combined single conditioning to the pair rep (line 13 - 14)
        cl_l, cl_m, atom_mask = convert_single_rep_to_blocks(
            ql=cl, n_query=self.n_query, n_key=self.n_key, atom_mask=batch["atom_mask"]
        )

        # Note to devs: in previous checkpoints before v13, linear_l and linear_m
        #  were reversed. Changed it for consistent naming.
        cl_lm = (
            self.linear_l(self.relu(cl_l.unsqueeze(-2)))
            + self.linear_m(self.relu(cl_m.unsqueeze(-3)))
        ) * atom_mask.unsqueeze(-1)

        # [*, N_blocks, N_query, N_key, c_atom_pair]
        plm = plm + cl_lm

        plm = plm + self.pair_mlp(plm)

        plm = plm * atom_mask.unsqueeze(-1)

        return ql, cl, plm

    def forward(
        self,
        batch: TensorDict,
        rl: torch.Tensor | None = None,
        si_trunk: torch.Tensor | None = None,
        zij_trunk: torch.Tensor | None = None,
        use_high_precision_attention: bool = False,
    ) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
        """
        Args:
            batch:
                Input feature dictionary. Features used in this function:
                    - "ref_pos": [*, N_atom, 3] atom positions in the
                        reference conformer
                    - "ref_mask": [*, N_atom] atom mask for the reference conformer
                    - "ref_element": [*, N_atom, 128] one-hot encoding of atomic number
                        in the reference conformer
                    - "ref_charge": [*, N_atom] atom charge in the reference conformer
                    - "ref_atom_name_chars": [*, N_atom, 4, 64] one-hot encoding of
                        unicode integers representing unique atom names in the
                        reference conformer
                    - "ref_space_uid": [*, n_atom,] numerical encoding of the chain id
                        and residue index in the reference conformer
                    - "token_mask": [*, N_token] token mask
                    - "num_atoms_per_token": [*, N_token] Number of atoms per token
            rl:
                [*, N_atom, 3] Noisy atom positions (optional)
            si_trunk:
                [*, N_atom, c_s] Trunk single representation (optional)
            zij_trunk:
                [*, N_atom, N_atom, c_z] Trunk pair representation (optional)
            use_high_precision_attention:
                Whether to run attention in high precision
        Returns:
            ai:
                [*, N_token, c_token] Token representation
            ql:
                [*, N_atom, c_atom] Atom single representation
            cl:
                [*, N_atom, c_atom] Atom single conditioning
            plm:
                [*, N_blocks, N_query, N_key, c_atom_pair] Atom pair representation
                Note: Converted to block format ahead of time due to reduce memory cost
        """
        atom_mask = batch["atom_mask"]  # Padding mask

        atom_feat_args = (
            batch,
            rl,
            si_trunk,
            zij_trunk,
        )
        ql, cl, plm = checkpoint_section(
            fn=self.get_atom_reps,
            args=atom_feat_args,
            apply_ckpt=self.ckpt_intermediate_steps,
            use_reentrant=self.use_reentrant,
        )

        # Cross attention transformer (line 15)
        # [*, N_blocks, N_query, c_atom]
        ql = self.atom_transformer(
            a=ql,
            s=cl,
            z=plm,
            mask=atom_mask,
            use_high_precision_attention=use_high_precision_attention,
        )

        ql = ql * atom_mask.unsqueeze(-1)

        agg_args = (
            batch["token_mask"],
            batch["atom_to_token_index"],
            atom_mask,
            self.linear_q(ql),
            -2,
            "mean",
        )
        ai = checkpoint_section(
            fn=aggregate_atom_feat_to_tokens,
            args=agg_args,
            apply_ckpt=self.ckpt_intermediate_steps,
            use_reentrant=self.use_reentrant,
        )

        return ai, ql, cl, plm


class AtomAttentionDecoder(nn.Module):
    """
    Implements AF3 Algorithm 6.
    """

    def __init__(
        self,
        c_atom: int,
        c_atom_pair: int,
        c_token: int,
        c_hidden: int,
        no_heads: int,
        no_blocks: int,
        n_transition: int,
        n_query: int,
        n_key: int,
        use_ada_layer_norm: bool,
        blocks_per_ckpt: int | None = None,
        inf: float = 1e9,
        linear_init_params: ConfigDict = lin_init.atom_att_dec_init,
        use_reentrant: bool | None = None,
    ):
        """
        Args:
            c_atom:
                Atom single representation channel dimension
            c_atom_pair:
                Atom pair representation channel dimension
            c_token:
                Token single representation channel dimension
            c_hidden:
                Hidden channel dimension
            no_heads:
                Number of attention heads
            no_blocks:
                Number of attention blocks
            n_transition:
                Number of transition blocks
            n_query:
                Number of queries (block height)
            n_key:
                Number of keys (block width)
            use_ada_layer_norm:
                Whether to apply AdaLN-Zero conditioning
            blocks_per_ckpt:
                Number of blocks per checkpoint. If set, checkpointing will
                be used to save memory.
            inf:
                Large number used for attention masking
            linear_init_params:
                Linear layer initialization parameters
            use_reentrant:
                Whether to use reentrant variant of checkpointing. If set,
                torch checkpointing will be used (DeepSpeed does not support
                this feature)
        """
        super().__init__()

        self.inf = inf

        self.linear_q_in = Linear(c_token, c_atom, **linear_init_params.linear_q_in)

        self.atom_transformer = DiffusionTransformer(
            c_a=c_atom,
            c_s=c_atom,
            c_z=c_atom_pair,
            c_hidden=c_hidden,
            no_heads=no_heads,
            no_blocks=no_blocks,
            n_transition=n_transition,
            use_ada_layer_norm=use_ada_layer_norm,
            n_query=n_query,
            n_key=n_key,
            blocks_per_ckpt=blocks_per_ckpt,
            inf=self.inf,
            linear_init_params=linear_init_params.diffusion_transformer,
            use_reentrant=use_reentrant,
        )

        self.layer_norm = LayerNorm(c_in=c_atom, create_offset=False)
        self.linear_q_out = Linear(c_atom, 3, **linear_init_params.linear_q_out)

    def forward(
        self,
        batch: TensorDict,
        ai: torch.Tensor,
        ql: torch.Tensor,
        cl: torch.Tensor,
        plm: torch.Tensor,
        use_high_precision_attention: bool = False,
    ) -> torch.Tensor:
        """
        Args:
            batch:
                Input feature dictionary. Features used in this function:
                    - "token_mask": [*, N_token] Token mask
                    - "num_atoms_per_token": [*, N_token] Number of atoms per token
            ai:
                [*, N_token, c_token] Token representation
            ql:
                [*, N_atom, c_atom] Atom single representation
            cl:
                [*, N_atom, c_atom] Atom single conditioning
            plm:
                [*, N_blocks, N_query, N_key, c_atom_pair] Atom pair representation
                Note: Converted to block format in AtomAttentionEncoder
            use_high_precision_attention:
                Whether to run attention in high precision
        Returns:
            rl_update:
                [*, N_atom, 3] Atom position updates
        """
        # Broadcast per-token activations to atoms
        # [*, N_atom, c_atom]
        ql = ql + broadcast_token_feat_to_atoms(
            token_mask=batch["token_mask"],
            num_atoms_per_token=batch["num_atoms_per_token"],
            token_feat=self.linear_q_in(ai),
            token_dim=-2,
        )

        # Atom transformer
        # [*, N_atom, c_atom]
        ql = self.atom_transformer(
            a=ql,
            s=cl,
            z=plm,
            mask=batch["atom_mask"],  # Padding mask
            use_high_precision_attention=use_high_precision_attention,
        )

        # Compute updates for atom positions
        # [*, N_atom, 3]
        rl_update = self.linear_q_out(self.layer_norm(ql))

        return rl_update
