





"""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__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							...






@overload
def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							...






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase			):
							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(
														    UpperCamelCase   ,    all_atom_masks[..., ca_idx]   ,    all_atom_masks[..., cb_idx]   ,    )
														return pseudo_beta, pseudo_beta_mask
							else:
														return pseudo_beta






def    A__							(     UpperCamelCase   ,    UpperCamelCase			):
							A						=    batched_gather(
							    UpperCamelCase   ,    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    A__							(     UpperCamelCase			):
							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(UpperCamelCase   ,    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__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase = False   ,    UpperCamelCase = 1E-20   ,    UpperCamelCase = 1E8   ,    ):
							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=UpperCamelCase			)
							A						=    torch.linspace(UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    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]   ,    UpperCamelCase   ,    -1   ,    -1			)			)
							to_concat.append(aatype_one_hot[..., None, :].expand(*aatype_one_hot.shape[:-2]   ,    -1   ,    UpperCamelCase   ,    -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=UpperCamelCase   ,    )
							A						=    rigids.get_trans()[..., None, :, :]
							A						=    rigids[..., None].invert_apply(UpperCamelCase			)

							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(UpperCamelCase   ,    dim=-1			)
							A						=    act * template_mask_ad[..., None]

							return act






def    A__							(     UpperCamelCase			):
							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(UpperCamelCase   ,    dim=-1			)






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    ):
							# [*, N, 8, 4, 4]
							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(UpperCamelCase			)

							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=UpperCamelCase			)   ,    UpperCamelCase			)			)

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

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

							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(UpperCamelCase			)

							return all_frames_to_global






def    A__							(     UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    UpperCamelCase   ,    ):
							# [*, N, 14]
							A						=    group_idx[aatype, ...]

							# [*, N, 14, 8]
							A						=    nn.functional.one_hot(
							    UpperCamelCase   ,    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 UpperCamelCase			: torch.sum(UpperCamelCase   ,    dim=-1			)			)

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

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

							return pred_positions









"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_snake_case	:						Any					=					{
    'configuration_unispeech_sat': ['UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechSatConfig'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						str					=					[
		    'UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'UniSpeechSatForAudioFrameClassification',
		    'UniSpeechSatForCTC',
		    'UniSpeechSatForPreTraining',
		    'UniSpeechSatForSequenceClassification',
		    'UniSpeechSatForXVector',
		    'UniSpeechSatModel',
		    'UniSpeechSatPreTrainedModel',
		]

if TYPE_CHECKING:
		from .configuration_unispeech_sat import UNISPEECH_SAT_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechSatConfig

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_unispeech_sat import (
				    UNISPEECH_SAT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    UniSpeechSatForAudioFrameClassification,
				    UniSpeechSatForCTC,
				    UniSpeechSatForPreTraining,
				    UniSpeechSatForSequenceClassification,
				    UniSpeechSatForXVector,
				    UniSpeechSatModel,
				    UniSpeechSatPreTrainedModel,
				)

else:
		import sys

		_snake_case	:						Union[str, Any]					=					_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


