
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 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    ...





@overload
def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    ...





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			aatype == rc.restype_order["G"]
    snake_case__  =			rc.atom_order["CA"]
    snake_case__  =			rc.atom_order["CB"]
    snake_case__  =			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:
        snake_case__  =			torch.where(
            __lowerCAmelCase      ,						all_atom_masks[..., ca_idx]      ,						all_atom_masks[..., cb_idx]      ,						)
        return pseudo_beta, pseudo_beta_mask
    else:
        return pseudo_beta





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase							):
    snake_case__  =			batched_gather(
        __lowerCAmelCase      ,						batch["residx_atom37_to_atom14"]      ,						dim=-2      ,						no_batch_dims=len(atomaa.shape[:-2]							)      ,						)

    snake_case__  =			atomaa_data * batch["atom37_atom_exists"][..., None]

    return atomaa_data





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			template_feats["template_aatype"]
    snake_case__  =			template_feats["template_torsion_angles_sin_cos"]
    snake_case__  =			template_feats["template_alt_torsion_angles_sin_cos"]
    snake_case__  =			template_feats["template_torsion_angles_mask"]
    snake_case__  =			torch.cat(
        [
            nn.functional.one_hot(__lowerCAmelCase      ,						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 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase = False      ,						__lowerCAmelCase = 1E-20      ,						__lowerCAmelCase = 1E8      ,						):
    snake_case__  =			batch["template_pseudo_beta_mask"]
    snake_case__  =			template_mask[..., None] * template_mask[..., None, :]

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

    snake_case__  =			[dgram, template_mask_ad[..., None]]

    snake_case__  =			nn.functional.one_hot(
        batch["template_aatype"]      ,						rc.restype_num + 2      ,						)

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

    snake_case__  ,   snake_case__  ,   snake_case__  =			[rc.atom_order[a] for a in ["N", "CA", "C"]]
    snake_case__  =			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=__lowerCAmelCase      ,						)
    snake_case__  =			rigids.get_trans()[..., None, :, :]
    snake_case__  =			rigids[..., None].invert_apply(__lowerCAmelCase							)

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

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

    snake_case__  =			inv_distance_scalar * template_mask_ad
    snake_case__  =			rigid_vec * inv_distance_scalar[..., None]

    if not use_unit_vector:
        snake_case__  =			unit_vector * 0.0

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

    snake_case__  =			torch.cat(__lowerCAmelCase      ,						dim=-1							)
    snake_case__  =			act * template_mask_ad[..., None]

    return act





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase							):
    snake_case__  =			nn.functional.one_hot(batch["extra_msa"]      ,						23							)
    snake_case__  =			[
        msa_ahot,
        batch["extra_has_deletion"].unsqueeze(-1							),
        batch["extra_deletion_value"].unsqueeze(-1							),
    ]
    return torch.cat(__lowerCAmelCase      ,						dim=-1							)





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						):
    # [*, N, 8, 4, 4]
    snake_case__  =			rrgdf[aatype, ...]

    # [*, N, 8] transformations, i.e.
    #   One [*, N, 8, 3, 3] rotation matrix and
    #   One [*, N, 8, 3]    translation matrix
    snake_case__  =			r.from_tensor_axa(__lowerCAmelCase							)

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

    # [*, N, 8, 2]
    snake_case__  =			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.

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

    snake_case__  =			default_r.compose(Rigid(Rotation(rot_mats=__lowerCAmelCase							)      ,						__lowerCAmelCase							)							)

    snake_case__  =			all_frames[..., 5]
    snake_case__  =			all_frames[..., 6]
    snake_case__  =			all_frames[..., 7]

    snake_case__  =			all_frames[..., 4]
    snake_case__  =			chia_frame_to_bb.compose(__lowerCAmelCase							)
    snake_case__  =			chia_frame_to_bb.compose(__lowerCAmelCase							)
    snake_case__  =			chia_frame_to_bb.compose(__lowerCAmelCase							)

    snake_case__  =			Rigid.cat(
        [
            all_frames[..., :5],
            chia_frame_to_bb.unsqueeze(-1							),
            chia_frame_to_bb.unsqueeze(-1							),
            chia_frame_to_bb.unsqueeze(-1							),
        ]      ,						dim=-1      ,						)

    snake_case__  =			r[..., None].compose(__lowerCAmelCase							)

    return all_frames_to_global





def 		SCREAMING_SNAKE_CASE__      ( __lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						__lowerCAmelCase      ,						):
    # [*, N, 14]
    snake_case__  =			group_idx[aatype, ...]

    # [*, N, 14, 8]
    snake_case__  =			nn.functional.one_hot(
        __lowerCAmelCase      ,						num_classes=default_frames.shape[-3]      ,						)

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

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

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

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

    return pred_positions





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__magic_name__           =					logging.get_logger(__name__)

__magic_name__           =					{
    '''MBZUAI/swiftformer-xs''': '''https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json''',
}




class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						Any      		=       'swiftformer'
      def __init__(     self						,		lowerCamelCase=3						,		lowerCamelCase=[3, 3, 6, 4]						,		lowerCamelCase=[48, 56, 1_12, 2_20]						,		lowerCamelCase=4						,		lowerCamelCase=[True, True, True, True]						,		lowerCamelCase="gelu"						,		lowerCamelCase=3						,		lowerCamelCase=2						,		lowerCamelCase=1						,		lowerCamelCase=0.0						,		lowerCamelCase=True						,		lowerCamelCase=1e-5						,		lowerCamelCase=1e-5						,		**lowerCamelCase						,		):
          super().__init__(**lowerCamelCase	)
          snake_case__  =			num_channels
          snake_case__  =			depths
          snake_case__  =			embed_dims
          snake_case__  =			mlp_ratio
          snake_case__  =			downsamples
          snake_case__  =			hidden_act
          snake_case__  =			down_patch_size
          snake_case__  =			down_stride
          snake_case__  =			down_pad
          snake_case__  =			drop_path_rate
          snake_case__  =			use_layer_scale
          snake_case__  =			layer_scale_init_value
          snake_case__  =			batch_norm_eps





class    _SCREAMING_SNAKE_CASE					( __UpperCamelCase	):
      _A  :						int      		=       version.parse('1.11'	)
      @property
      def A_     (     self	):
          return OrderedDict(
              [
                  ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
              ]	)



      @property
      def A_     (     self	):
          return 1e-4



