




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       __magic_name__    (			A		: torch.Tensor,  A		: torch.Tensor,  A		: None       ):






			'''simple docstring'''







			...




@overload
def       __magic_name__    (			A		: torch.Tensor,  A		: torch.Tensor,  A		: torch.Tensor       ):






			'''simple docstring'''







			...




def       __magic_name__    (			A		: List[str],  A		: Optional[int],  A		: Dict       ):






			'''simple docstring'''







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




def       __magic_name__    (			A		: torch.Tensor,  A		: Dict[str, torch.Tensor]       ):






			'''simple docstring'''







			a   =      batched_gather(
			    A,  batch["residx_atom37_to_atom14"],  dim=-2,  no_batch_dims=len(atomaa.shape[:-2]       ),  )

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

			return atomaa_data




def       __magic_name__    (			A		: Dict[str, torch.Tensor]       ):






			'''simple docstring'''







			a   =      template_feats["template_aatype"]
			a   =      template_feats["template_torsion_angles_sin_cos"]
			a   =      template_feats["template_alt_torsion_angles_sin_cos"]
			a   =      template_feats["template_torsion_angles_mask"]
			a   =      torch.cat(
			    [
			        nn.functional.one_hot(A,  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       __magic_name__    (			A		: Dict[str, torch.Tensor],  A		: torch.types.Number,  A		: torch.types.Number,  A		: int,  A		: bool = False,  A		: float = 1E-20,  A		: float = 1E8,  ):






			'''simple docstring'''







			a   =      batch["template_pseudo_beta_mask"]
			a   =      template_mask[..., None] * template_mask[..., None, :]

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

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

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

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

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

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

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

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

			if not use_unit_vector:
						a   =      unit_vector * 0.0

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

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

			return act




def       __magic_name__    (			A		: Dict[str, torch.Tensor]       ):






			'''simple docstring'''







			a   =      nn.functional.one_hot(batch["extra_msa"],  23       )
			a   =      [
			    msa_ahot,
			    batch["extra_has_deletion"].unsqueeze(-1       ),
			    batch["extra_deletion_value"].unsqueeze(-1       ),
			]
			return torch.cat(A,  dim=-1       )




def       __magic_name__    (			A		: Rigid,  A		: torch.Tensor,  A		: torch.Tensor,  A		: torch.Tensor,  ):






			'''simple docstring'''







			a   =      rrgdf[aatype, ...]

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

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

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

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

			a   =      default_r.compose(Rigid(Rotation(rot_mats=A       ),  A       )       )

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

			a   =      all_frames[..., 4]
			a   =      chia_frame_to_bb.compose(A       )
			a   =      chia_frame_to_bb.compose(A       )
			a   =      chia_frame_to_bb.compose(A       )

			a   =      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,  )

			a   =      r[..., None].compose(A       )

			return all_frames_to_global






def       __magic_name__    (			A		: Rigid,  A		: torch.Tensor,  A		: torch.Tensor,  A		: torch.Tensor,  A		: torch.Tensor,  A		: torch.Tensor,  ):






			'''simple docstring'''







			a   =      group_idx[aatype, ...]

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

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

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

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

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

			return pred_positions












import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
		import torch

		from transformers import CamembertModel




@require_torch
@require_sentencepiece
@require_tokenizers
class    snake_case__							(unittest.TestCase       ):
				"""simple docstring"""



				@slow
				def 							__UpperCAmelCase							(				self    :					Optional[Any]							) ->					List[str]:
							a   =      CamembertModel.from_pretrained("camembert-base"							)
							model.to(__lowerCamelCase							)

							a   =      torch.tensor(
							    [[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] ,				device=__lowerCamelCase ,				dtype=torch.long ,				)  # J'aime le camembert !
							with torch.no_grad():
										a   =      model(__lowerCamelCase							)["last_hidden_state"]
							a   =      torch.Size((1, 10, 7_68)							)
							self.assertEqual(output.shape ,				__lowerCamelCase							)
							# compare the actual values for a slice.
							a   =      torch.tensor(
							    [[[-0.0_254, 0.0_235, 0.1_027], [0.0_606, -0.1_811, -0.0_418], [-0.1_561, -0.1_127, 0.2_687]]] ,				device=__lowerCamelCase ,				dtype=torch.float ,				)
							# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
							# camembert.eval()
							# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

							self.assertTrue(torch.allclose(output[:, :3, :3] ,				__lowerCamelCase ,				atol=1e-4							)							)






