# What does this PR do?

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<!-- Remove if not applicable -->

Set the `TF32` flag for both PyTorch's CUDA and cuDNN backend.

Currently, the `TrainingArguments` parser only sets the `TF32` flag for the CUDA backend. The user can manually pass `--tf32 False` in the command line, but the `torch.backends.cudnn.allow_tf32` would remain `True` during training. There are also some test cases in that we manually set `torch.backends.cuda.matmul.allow_tf32 = False`.

NOTE: The default value of `torch.backends.cudnn.allow_tf32` for the cuDNN backend is `True` (was added 3 years ago).

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [X] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [X] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

Set `TF32` flag for PyTorch cuDNN backend. # What does this PR do?

<!--
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Once merged, your PR is going to appear in the release notes with the title you set, so make sure it's a great title that fully reflects the extent of your awesome contribution.

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Once you're done, someone will review your PR shortly (see the section "Who can review?" below to tag some potential reviewers). They may suggest changes to make the code even better. If no one reviewed your PR after a week has passed, don't hesitate to post a new comment @-mentioning the same persons---sometimes notifications get lost.
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<!-- Remove if not applicable -->

Set the `TF32` flag for both PyTorch's CUDA and cuDNN backend.

Currently, the `TrainingArguments` parser only sets the `TF32` flag for the CUDA backend. The user can manually pass `--tf32 False` in the command line, but the `torch.backends.cudnn.allow_tf32` would remain `True` during training. There are also some test cases in that we manually set `torch.backends.cuda.matmul.allow_tf32 = False`.

NOTE: The default value of `torch.backends.cudnn.allow_tf32` for the cuDNN backend is `True` (was added 3 years ago).

## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
- [X] Did you read the [contributor guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the [forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [X] Did you make sure to update the documentation with your changes? Here are the
      [documentation guidelines](https://github.com/huggingface/transformers/tree/main/docs), and
      [here are tips on formatting docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [X] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

<!-- Your PR will be replied to more quickly if you can figure out the right person to tag with @

 If you know how to use git blame, that is the easiest way, otherwise, here is a rough guide of **who to tag**.
 Please tag fewer than 3 people.

Models:

- text models: @ArthurZucker and @younesbelkada
- vision models: @amyeroberts
- speech models: @sanchit-gandhi
- graph models: @clefourrier

Library:

- flax: @sanchit-gandhi
- generate: @gante
- pipelines: @Narsil
- tensorflow: @gante and @Rocketknight1
- tokenizers: @ArthurZucker
- trainer: @sgugger

Integrations:

- deepspeed: HF Trainer/Accelerate: @pacman100
- ray/raytune: @richardliaw, @amogkam

Documentation: @sgugger, @stevhliu and @MKhalusova

HF projects:

- accelerate: [different repo](https://github.com/huggingface/accelerate)
- datasets: [different repo](https://github.com/huggingface/datasets)
- diffusers: [different repo](https://github.com/huggingface/diffusers)
- rust tokenizers: [different repo](https://github.com/huggingface/tokenizers)

Maintained examples (not research project or legacy):

- Flax: @sanchit-gandhi
- PyTorch: @sgugger
- TensorFlow: @Rocketknight1

 -->

# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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.

from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather


@overload
def pseudo_beta_fn(aatype: torch.Tensor, all_atom_positions: torch.Tensor, all_atom_masks: None) -> torch.Tensor:
    ...


@overload
def pseudo_beta_fn(
    aatype: torch.Tensor, all_atom_positions: torch.Tensor, all_atom_masks: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    ...


def pseudo_beta_fn(aatype, all_atom_positions, all_atom_masks):
    is_gly = aatype == rc.restype_order["G"]
    ca_idx = rc.atom_order["CA"]
    cb_idx = rc.atom_order["CB"]
    pseudo_beta = torch.where(
        is_gly[..., None].expand(*((-1,) * len(is_gly.shape)), 3),
        all_atom_positions[..., ca_idx, :],
        all_atom_positions[..., cb_idx, :],
    )

    if all_atom_masks is not None:
        pseudo_beta_mask = torch.where(
            is_gly,
            all_atom_masks[..., ca_idx],
            all_atom_masks[..., cb_idx],
        )
        return pseudo_beta, pseudo_beta_mask
    else:
        return pseudo_beta


def atom14_to_atom37(atom14: torch.Tensor, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
    atom37_data = batched_gather(
        atom14,
        batch["residx_atom37_to_atom14"],
        dim=-2,
        no_batch_dims=len(atom14.shape[:-2]),
    )

    atom37_data = atom37_data * batch["atom37_atom_exists"][..., None]

    return atom37_data


def build_template_angle_feat(template_feats: Dict[str, torch.Tensor]) -> torch.Tensor:
    template_aatype = template_feats["template_aatype"]
    torsion_angles_sin_cos = template_feats["template_torsion_angles_sin_cos"]
    alt_torsion_angles_sin_cos = template_feats["template_alt_torsion_angles_sin_cos"]
    torsion_angles_mask = template_feats["template_torsion_angles_mask"]
    template_angle_feat = torch.cat(
        [
            nn.functional.one_hot(template_aatype, 22),
            torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2], 14),
            alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2], 14),
            torsion_angles_mask,
        ],
        dim=-1,
    )

    return template_angle_feat


def build_template_pair_feat(
    batch: Dict[str, torch.Tensor],
    min_bin: torch.types.Number,
    max_bin: torch.types.Number,
    no_bins: int,
    use_unit_vector: bool = False,
    eps: float = 1e-20,
    inf: float = 1e8,
) -> torch.Tensor:
    template_mask = batch["template_pseudo_beta_mask"]
    template_mask_2d = template_mask[..., None] * template_mask[..., None, :]

    # Compute distogram (this seems to differ slightly from Alg. 5)
    tpb = batch["template_pseudo_beta"]
    dgram = torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2, dim=-1, keepdim=True)
    lower = torch.linspace(min_bin, max_bin, no_bins, device=tpb.device) ** 2
    upper = torch.cat([lower[1:], lower.new_tensor([inf])], dim=-1)
    dgram = ((dgram > lower) * (dgram < upper)).type(dgram.dtype)

    to_concat = [dgram, template_mask_2d[..., None]]

    aatype_one_hot: torch.LongTensor = nn.functional.one_hot(
        batch["template_aatype"],
        rc.restype_num + 2,
    )

    n_res = batch["template_aatype"].shape[-1]
    to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2], n_res, -1, -1))
    to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2], -1, n_res, -1))

    n, ca, c = [rc.atom_order[a] for a in ["N", "CA", "C"]]
    rigids = Rigid.make_transform_from_reference(
        n_xyz=batch["template_all_atom_positions"][..., n, :],
        ca_xyz=batch["template_all_atom_positions"][..., ca, :],
        c_xyz=batch["template_all_atom_positions"][..., c, :],
        eps=eps,
    )
    points = rigids.get_trans()[..., None, :, :]
    rigid_vec = rigids[..., None].invert_apply(points)

    inv_distance_scalar = torch.rsqrt(eps + torch.sum(rigid_vec**2, dim=-1))

    t_aa_masks = batch["template_all_atom_mask"]
    template_mask = t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
    template_mask_2d = template_mask[..., None] * template_mask[..., None, :]

    inv_distance_scalar = inv_distance_scalar * template_mask_2d
    unit_vector = rigid_vec * inv_distance_scalar[..., None]

    if not use_unit_vector:
        unit_vector = unit_vector * 0.0

    to_concat.extend(torch.unbind(unit_vector[..., None, :], dim=-1))
    to_concat.append(template_mask_2d[..., None])

    act = torch.cat(to_concat, dim=-1)
    act = act * template_mask_2d[..., None]

    return act


def build_extra_msa_feat(batch: Dict[str, torch.Tensor]) -> torch.Tensor:
    msa_1hot: torch.LongTensor = nn.functional.one_hot(batch["extra_msa"], 23)
    msa_feat = [
        msa_1hot,
        batch["extra_has_deletion"].unsqueeze(-1),
        batch["extra_deletion_value"].unsqueeze(-1),
    ]
    return torch.cat(msa_feat, dim=-1)


def torsion_angles_to_frames(
    r: Rigid,
    alpha: torch.Tensor,
    aatype: torch.Tensor,
    rrgdf: torch.Tensor,
) -> Rigid:
    # [*, N, 8, 4, 4]
    default_4x4 = rrgdf[aatype, ...]

    # [*, N, 8] transformations, i.e.
    #   One [*, N, 8, 3, 3] rotation matrix and
    #   One [*, N, 8, 3]    translation matrix
    default_r = r.from_tensor_4x4(default_4x4)

    bb_rot = alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2))
    bb_rot[..., 1] = 1

    # [*, N, 8, 2]
    alpha = torch.cat([bb_rot.expand(*alpha.shape[:-2], -1, -1), alpha], dim=-2)

    # [*, N, 8, 3, 3]
    # Produces rotation matrices of the form:
    # [
    #   [1, 0  , 0  ],
    #   [0, a_2,-a_1],
    #   [0, a_1, a_2]
    # ]
    # This follows the original code rather than the supplement, which uses
    # different indices.

    all_rots = alpha.new_zeros(default_r.get_rots().get_rot_mats().shape)
    all_rots[..., 0, 0] = 1
    all_rots[..., 1, 1] = alpha[..., 1]
    all_rots[..., 1, 2] = -alpha[..., 0]
    all_rots[..., 2, 1:] = alpha

    all_frames = default_r.compose(Rigid(Rotation(rot_mats=all_rots), None))

    chi2_frame_to_frame = all_frames[..., 5]
    chi3_frame_to_frame = all_frames[..., 6]
    chi4_frame_to_frame = all_frames[..., 7]

    chi1_frame_to_bb = all_frames[..., 4]
    chi2_frame_to_bb = chi1_frame_to_bb.compose(chi2_frame_to_frame)
    chi3_frame_to_bb = chi2_frame_to_bb.compose(chi3_frame_to_frame)
    chi4_frame_to_bb = chi3_frame_to_bb.compose(chi4_frame_to_frame)

    all_frames_to_bb = Rigid.cat(
        [
            all_frames[..., :5],
            chi2_frame_to_bb.unsqueeze(-1),
            chi3_frame_to_bb.unsqueeze(-1),
            chi4_frame_to_bb.unsqueeze(-1),
        ],
        dim=-1,
    )

    all_frames_to_global = r[..., None].compose(all_frames_to_bb)

    return all_frames_to_global


def frames_and_literature_positions_to_atom14_pos(
    r: Rigid,
    aatype: torch.Tensor,
    default_frames: torch.Tensor,
    group_idx: torch.Tensor,
    atom_mask: torch.Tensor,
    lit_positions: torch.Tensor,
) -> torch.Tensor:
    # [*, N, 14]
    group_mask = group_idx[aatype, ...]

    # [*, N, 14, 8]
    group_mask_one_hot: torch.LongTensor = nn.functional.one_hot(
        group_mask,
        num_classes=default_frames.shape[-3],
    )

    # [*, N, 14, 8]
    t_atoms_to_global = r[..., None, :] * group_mask_one_hot

    # [*, N, 14]
    t_atoms_to_global = t_atoms_to_global.map_tensor_fn(lambda x: torch.sum(x, dim=-1))

    # [*, N, 14, 1]
    atom_mask = atom_mask[aatype, ...].unsqueeze(-1)

    # [*, N, 14, 3]
    lit_positions = lit_positions[aatype, ...]
    pred_positions = t_atoms_to_global.apply(lit_positions)
    pred_positions = pred_positions * atom_mask

    return pred_positions

# Copyright 2021 AlQuraishi Laboratory
# Copyright 2021 DeepMind Technologies Limited
#
# 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.

from typing import Dict, Tuple, overload

import torch
import torch.types
from torch import nn

from . import residue_constants as rc
from .rigid_utils import Rigid, Rotation
from .tensor_utils import batched_gather


@overload
def pseudo_beta_fn(aatype: torch.Tensor, all_atom_positions: torch.Tensor, all_atom_masks: None) -> torch.Tensor:
    ...


@overload
def pseudo_beta_fn(
    aatype: torch.Tensor, all_atom_positions: torch.Tensor, all_atom_masks: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
    ...


def pseudo_beta_fn(aatype, all_atom_positions, all_atom_masks):
    is_gly = aatype == rc.restype_order["G"]
    ca_idx = rc.atom_order["CA"]
    cb_idx = rc.atom_order["CB"]
    pseudo_beta = torch.where(
        is_gly[..., None].expand(*((-1,) * len(is_gly.shape)), 3),
        all_atom_positions[..., ca_idx, :],
        all_atom_positions[..., cb_idx, :],
    )

    if all_atom_masks is not None:
        pseudo_beta_mask = torch.where(
            is_gly,
            all_atom_masks[..., ca_idx],
            all_atom_masks[..., cb_idx],
        )
        return pseudo_beta, pseudo_beta_mask
    else:
        return pseudo_beta


def atom14_to_atom37(atom14: torch.Tensor, batch: Dict[str, torch.Tensor]) -> torch.Tensor:
    atom37_data = batched_gather(
        atom14,
        batch["residx_atom37_to_atom14"],
        dim=-2,
        no_batch_dims=len(atom14.shape[:-2]),
    )

    atom37_data = atom37_data * batch["atom37_atom_exists"][..., None]

    return atom37_data


def build_template_angle_feat(template_feats: Dict[str, torch.Tensor]) -> torch.Tensor:
    template_aatype = template_feats["template_aatype"]
    torsion_angles_sin_cos = template_feats["template_torsion_angles_sin_cos"]
    alt_torsion_angles_sin_cos = template_feats["template_alt_torsion_angles_sin_cos"]
    torsion_angles_mask = template_feats["template_torsion_angles_mask"]
    template_angle_feat = torch.cat(
        [
            nn.functional.one_hot(template_aatype, 22),
            torsion_angles_sin_cos.reshape(*torsion_angles_sin_cos.shape[:-2], 14),
            alt_torsion_angles_sin_cos.reshape(*alt_torsion_angles_sin_cos.shape[:-2], 14),
            torsion_angles_mask,
        ],
        dim=-1,
    )

    return template_angle_feat


def build_template_pair_feat(
    batch: Dict[str, torch.Tensor],
    min_bin: torch.types.Number,
    max_bin: torch.types.Number,
    no_bins: int,
    use_unit_vector: bool = False,
    eps: float = 1e-20,
    inf: float = 1e8,
) -> torch.Tensor:
    template_mask = batch["template_pseudo_beta_mask"]
    template_mask_2d = template_mask[..., None] * template_mask[..., None, :]

    # Compute distogram (this seems to differ slightly from Alg. 5)
    tpb = batch["template_pseudo_beta"]
    dgram = torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2, dim=-1, keepdim=True)
    lower = torch.linspace(min_bin, max_bin, no_bins, device=tpb.device) ** 2
    upper = torch.cat([lower[1:], lower.new_tensor([inf])], dim=-1)
    dgram = ((dgram > lower) * (dgram < upper)).type(dgram.dtype)

    to_concat = [dgram, template_mask_2d[..., None]]

    aatype_one_hot: torch.LongTensor = nn.functional.one_hot(
        batch["template_aatype"],
        rc.restype_num + 2,
    )

    n_res = batch["template_aatype"].shape[-1]
    to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2], n_res, -1, -1))
    to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2], -1, n_res, -1))

    n, ca, c = [rc.atom_order[a] for a in ["N", "CA", "C"]]
    rigids = Rigid.make_transform_from_reference(
        n_xyz=batch["template_all_atom_positions"][..., n, :],
        ca_xyz=batch["template_all_atom_positions"][..., ca, :],
        c_xyz=batch["template_all_atom_positions"][..., c, :],
        eps=eps,
    )
    points = rigids.get_trans()[..., None, :, :]
    rigid_vec = rigids[..., None].invert_apply(points)

    inv_distance_scalar = torch.rsqrt(eps + torch.sum(rigid_vec**2, dim=-1))

    t_aa_masks = batch["template_all_atom_mask"]
    template_mask = t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
    template_mask_2d = template_mask[..., None] * template_mask[..., None, :]

    inv_distance_scalar = inv_distance_scalar * template_mask_2d
    unit_vector = rigid_vec * inv_distance_scalar[..., None]

    if not use_unit_vector:
        unit_vector = unit_vector * 0.0

    to_concat.extend(torch.unbind(unit_vector[..., None, :], dim=-1))
    to_concat.append(template_mask_2d[..., None])

    act = torch.cat(to_concat, dim=-1)
    act = act * template_mask_2d[..., None]

    return act


def build_extra_msa_feat(batch: Dict[str, torch.Tensor]) -> torch.Tensor:
    msa_1hot: torch.LongTensor = nn.functional.one_hot(batch["extra_msa"], 23)
    msa_feat = [
        msa_1hot,
        batch["extra_has_deletion"].unsqueeze(-1),
        batch["extra_deletion_value"].unsqueeze(-1),
    ]
    return torch.cat(msa_feat, dim=-1)


def torsion_angles_to_frames(
    r: Rigid,
    alpha: torch.Tensor,
    aatype: torch.Tensor,
    rrgdf: torch.Tensor,
) -> Rigid:
    # [*, N, 8, 4, 4]
    default_4x4 = rrgdf[aatype, ...]

    # [*, N, 8] transformations, i.e.
    #   One [*, N, 8, 3, 3] rotation matrix and
    #   One [*, N, 8, 3]    translation matrix
    default_r = r.from_tensor_4x4(default_4x4)

    bb_rot = alpha.new_zeros((*((1,) * len(alpha.shape[:-1])), 2))
    bb_rot[..., 1] = 1

    # [*, N, 8, 2]
    alpha = torch.cat([bb_rot.expand(*alpha.shape[:-2], -1, -1), alpha], dim=-2)

    # [*, N, 8, 3, 3]
    # Produces rotation matrices of the form:
    # [
    #   [1, 0  , 0  ],
    #   [0, a_2,-a_1],
    #   [0, a_1, a_2]
    # ]
    # This follows the original code rather than the supplement, which uses
    # different indices.

    all_rots = alpha.new_zeros(default_r.get_rots().get_rot_mats().shape)
    all_rots[..., 0, 0] = 1
    all_rots[..., 1, 1] = alpha[..., 1]
    all_rots[..., 1, 2] = -alpha[..., 0]
    all_rots[..., 2, 1:] = alpha

    all_frames = default_r.compose(Rigid(Rotation(rot_mats=all_rots), None))

    chi2_frame_to_frame = all_frames[..., 5]
    chi3_frame_to_frame = all_frames[..., 6]
    chi4_frame_to_frame = all_frames[..., 7]

    chi1_frame_to_bb = all_frames[..., 4]
    chi2_frame_to_bb = chi1_frame_to_bb.compose(chi2_frame_to_frame)
    chi3_frame_to_bb = chi2_frame_to_bb.compose(chi3_frame_to_frame)
    chi4_frame_to_bb = chi3_frame_to_bb.compose(chi4_frame_to_frame)

    all_frames_to_bb = Rigid.cat(
        [
            all_frames[..., :5],
            chi2_frame_to_bb.unsqueeze(-1),
            chi3_frame_to_bb.unsqueeze(-1),
            chi4_frame_to_bb.unsqueeze(-1),
        ],
        dim=-1,
    )

    all_frames_to_global = r[..., None].compose(all_frames_to_bb)

    return all_frames_to_global


def frames_and_literature_positions_to_atom14_pos(
    r: Rigid,
    aatype: torch.Tensor,
    default_frames: torch.Tensor,
    group_idx: torch.Tensor,
    atom_mask: torch.Tensor,
    lit_positions: torch.Tensor,
) -> torch.Tensor:
    # [*, N, 14]
    group_mask = group_idx[aatype, ...]

    # [*, N, 14, 8]
    group_mask_one_hot: torch.LongTensor = nn.functional.one_hot(
        group_mask,
        num_classes=default_frames.shape[-3],
    )

    # [*, N, 14, 8]
    t_atoms_to_global = r[..., None, :] * group_mask_one_hot

    # [*, N, 14]
    t_atoms_to_global = t_atoms_to_global.map_tensor_fn(lambda x: torch.sum(x, dim=-1))

    # [*, N, 14, 1]
    atom_mask = atom_mask[aatype, ...].unsqueeze(-1)

    # [*, N, 14, 3]
    lit_positions = lit_positions[aatype, ...]
    pred_positions = t_atoms_to_global.apply(lit_positions)
    pred_positions = pred_positions * atom_mask

    return pred_positions
