





"""simple docstring"""




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 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						...





@overload
def 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						...





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   Tuple      =       aatype == rc.restype_order['''G''']
						_UpperCamelCase     :   Optional[Any]      =       rc.atom_order['''CA''']
						_UpperCamelCase     :   Dict      =       rc.atom_order['''CB''']
						_UpperCamelCase     :   Tuple      =       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:
												_UpperCamelCase     :   List[str]      =       torch.where(
												    _lowercase			,		all_atom_masks[..., ca_idx]			,		all_atom_masks[..., cb_idx]			,		)
												return pseudo_beta, pseudo_beta_mask
						else:
												return pseudo_beta





def 			a_							(				_lowercase			,		_lowercase  ):
						_UpperCamelCase     :   str      =       batched_gather(
						    _lowercase			,		batch['''residx_atom37_to_atom14''']			,		dim=-2			,		no_batch_dims=len(atomaa.shape[:-2]  )			,		)

						_UpperCamelCase     :   int      =       atomaa_data * batch['''atom37_atom_exists'''][..., None]

						return atomaa_data





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   str      =       template_feats['''template_aatype''']
						_UpperCamelCase     :   Optional[int]      =       template_feats['''template_torsion_angles_sin_cos''']
						_UpperCamelCase     :   Tuple      =       template_feats['''template_alt_torsion_angles_sin_cos''']
						_UpperCamelCase     :   List[str]      =       template_feats['''template_torsion_angles_mask''']
						_UpperCamelCase     :   Dict      =       torch.cat(
						    [
						        nn.functional.one_hot(_lowercase			,		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 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase = False			,		_lowercase = 1E-20			,		_lowercase = 1E8			,		):
						_UpperCamelCase     :   Optional[int]      =       batch['''template_pseudo_beta_mask''']
						_UpperCamelCase     :   Optional[Any]      =       template_mask[..., None] * template_mask[..., None, :]

						# Compute distogram (this seems to differ slightly from Alg. 5)
						_UpperCamelCase     :   Optional[int]      =       batch['''template_pseudo_beta''']
						_UpperCamelCase     :   Dict      =       torch.sum((tpb[..., None, :] - tpb[..., None, :, :]) ** 2			,		dim=-1			,		keepdim=_lowercase  )
						_UpperCamelCase     :   Dict      =       torch.linspace(_lowercase			,		_lowercase			,		_lowercase			,		device=tpb.device  ) ** 2
						_UpperCamelCase     :   List[Any]      =       torch.cat([lower[1:], lower.new_tensor([inf]  )]			,		dim=-1  )
						_UpperCamelCase     :   Optional[int]      =       ((dgram > lower) * (dgram < upper)).type(dgram.dtype  )

						_UpperCamelCase     :   Tuple      =       [dgram, template_mask_ad[..., None]]

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

						_UpperCamelCase     :   List[str]      =       batch['''template_aatype'''].shape[-1]
						to_concat.append(aatype_one_hot[..., None, :, :].expand(*aatype_one_hot.shape[:-2]			,		_lowercase			,		-1			,		-1  )  )
						to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]			,		-1			,		_lowercase			,		-1  )  )

						_UpperCamelCase  ,				_UpperCamelCase  ,				_UpperCamelCase     :   Dict      =       [rc.atom_order[a] for a in ['''N''', '''CA''', '''C''']]
						_UpperCamelCase     :   List[Any]      =       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=_lowercase			,		)
						_UpperCamelCase     :   str      =       rigids.get_trans()[..., None, :, :]
						_UpperCamelCase     :   Optional[Any]      =       rigids[..., None].invert_apply(_lowercase  )

						_UpperCamelCase     :   List[str]      =       torch.rsqrt(eps + torch.sum(rigid_vec**2			,		dim=-1  )  )

						_UpperCamelCase     :   List[str]      =       batch['''template_all_atom_mask''']
						_UpperCamelCase     :   int      =       t_aa_masks[..., n] * t_aa_masks[..., ca] * t_aa_masks[..., c]
						_UpperCamelCase     :   Union[str, Any]      =       template_mask[..., None] * template_mask[..., None, :]

						_UpperCamelCase     :   List[Any]      =       inv_distance_scalar * template_mask_ad
						_UpperCamelCase     :   int      =       rigid_vec * inv_distance_scalar[..., None]

						if not use_unit_vector:
												_UpperCamelCase     :   Any      =       unit_vector * 0.0

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

						_UpperCamelCase     :   int      =       torch.cat(_lowercase			,		dim=-1  )
						_UpperCamelCase     :   List[Any]      =       act * template_mask_ad[..., None]

						return act





def 			a_							(				_lowercase  ):
						_UpperCamelCase     :   torch.LongTensor      =       nn.functional.one_hot(batch['''extra_msa''']			,		23  )
						_UpperCamelCase     :   Optional[Any]      =       [
						    msa_ahot,
						    batch['''extra_has_deletion'''].unsqueeze(-1  ),
						    batch['''extra_deletion_value'''].unsqueeze(-1  ),
						]
						return torch.cat(_lowercase			,		dim=-1  )





def 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		):
						# [*, N, 8, 4, 4]
						_UpperCamelCase     :   List[str]      =       rrgdf[aatype, ...]

						# [*, N, 8] transformations, i.e.
						#   One [*, N, 8, 3, 3] rotation matrix and
						#   One [*, N, 8, 3]    translation matrix
						_UpperCamelCase     :   Union[str, Any]      =       r.from_tensor_axa(_lowercase  )

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

						# [*, N, 8, 2]
						_UpperCamelCase     :   Tuple      =       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.

						_UpperCamelCase     :   List[str]      =       alpha.new_zeros(default_r.get_rots().get_rot_mats().shape  )
						_UpperCamelCase     :   Any      =       1
						_UpperCamelCase     :   Tuple      =       alpha[..., 1]
						_UpperCamelCase     :   List[str]      =       -alpha[..., 0]
						_UpperCamelCase     :   Any      =       alpha

						_UpperCamelCase     :   int      =       default_r.compose(Rigid(Rotation(rot_mats=_lowercase  )			,		_lowercase  )  )

						_UpperCamelCase     :   int      =       all_frames[..., 5]
						_UpperCamelCase     :   int      =       all_frames[..., 6]
						_UpperCamelCase     :   Optional[Any]      =       all_frames[..., 7]

						_UpperCamelCase     :   Union[str, Any]      =       all_frames[..., 4]
						_UpperCamelCase     :   Any      =       chia_frame_to_bb.compose(_lowercase  )
						_UpperCamelCase     :   Union[str, Any]      =       chia_frame_to_bb.compose(_lowercase  )
						_UpperCamelCase     :   Union[str, Any]      =       chia_frame_to_bb.compose(_lowercase  )

						_UpperCamelCase     :   str      =       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			,		)

						_UpperCamelCase     :   List[str]      =       r[..., None].compose(_lowercase  )

						return all_frames_to_global






def 			a_							(				_lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		_lowercase			,		):
						# [*, N, 14]
						_UpperCamelCase     :   Optional[Any]      =       group_idx[aatype, ...]

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

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

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

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

						# [*, N, 14, 3]
						_UpperCamelCase     :   Union[str, Any]      =       lit_positions[aatype, ...]
						_UpperCamelCase     :   Any      =       t_atoms_to_global.apply(_lowercase  )
						_UpperCamelCase     :   Dict      =       pred_positions * atom_mask

						return pred_positions










"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase_	 ={
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
						if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""CamembertTokenizer"""]

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =["""CamembertTokenizerFast"""]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """CamembertForCausalLM""",
						    """CamembertForMaskedLM""",
						    """CamembertForMultipleChoice""",
						    """CamembertForQuestionAnswering""",
						    """CamembertForSequenceClassification""",
						    """CamembertForTokenClassification""",
						    """CamembertModel""",
						    """CamembertPreTrainedModel""",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						UpperCamelCase_	 =[
						    """TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TFCamembertForCausalLM""",
						    """TFCamembertForMaskedLM""",
						    """TFCamembertForMultipleChoice""",
						    """TFCamembertForQuestionAnswering""",
						    """TFCamembertForSequenceClassification""",
						    """TFCamembertForTokenClassification""",
						    """TFCamembertModel""",
						    """TFCamembertPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig

						try:
												if not is_sentencepiece_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert import CamembertTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_camembert_fast import CamembertTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_camembert import (
												    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    CamembertForCausalLM,
												    CamembertForMaskedLM,
												    CamembertForMultipleChoice,
												    CamembertForQuestionAnswering,
												    CamembertForSequenceClassification,
												    CamembertForTokenClassification,
												    CamembertModel,
												    CamembertPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_camembert import (
												    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFCamembertForCausalLM,
												    TFCamembertForMaskedLM,
												    TFCamembertForMultipleChoice,
												    TFCamembertForQuestionAnswering,
												    TFCamembertForSequenceClassification,
												    TFCamembertForTokenClassification,
												    TFCamembertModel,
												    TFCamembertPreTrainedModel,
												)

else:
						import sys

						UpperCamelCase_	 =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



